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| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| """ | |
| DETR Transformer class. | |
| Copy-paste from torch.nn.Transformer with modifications: | |
| * positional encodings are passed in MHattention | |
| * extra LN at the end of encoder is removed | |
| * decoder returns a stack of activations from all decoding layers | |
| """ | |
| import copy | |
| from typing import List, Optional | |
| from numpy import block | |
| import torch | |
| import torch.nn.functional as F | |
| from torch import Tensor, nn | |
| class SkipTransformerEncoder(nn.Module): | |
| def __init__(self, encoder_layer, num_layers, norm=None): | |
| super().__init__() | |
| self.d_model = encoder_layer.d_model | |
| self.num_layers = num_layers | |
| self.norm = norm | |
| assert num_layers % 2 == 1 | |
| num_block = (num_layers-1)//2 | |
| self.input_blocks = _get_clones(encoder_layer, num_block) | |
| self.middle_block = _get_clone(encoder_layer) | |
| self.output_blocks = _get_clones(encoder_layer, num_block) | |
| self.linear_blocks = _get_clones(nn.Linear(2*self.d_model, self.d_model), num_block) | |
| self._reset_parameters() | |
| def _reset_parameters(self): | |
| for p in self.parameters(): | |
| if p.dim() > 1: | |
| nn.init.xavier_uniform_(p) | |
| def forward(self, src, | |
| mask: Optional[Tensor] = None, | |
| src_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None): | |
| x = src | |
| xs = [] | |
| for module in self.input_blocks: | |
| x = module(x, src_mask=mask, | |
| src_key_padding_mask=src_key_padding_mask, pos=pos) | |
| xs.append(x) | |
| x = self.middle_block(x, src_mask=mask, | |
| src_key_padding_mask=src_key_padding_mask, pos=pos) | |
| for (module, linear) in zip(self.output_blocks, self.linear_blocks): | |
| x = torch.cat([x, xs.pop()], dim=-1) | |
| x = linear(x) | |
| x = module(x, src_mask=mask, | |
| src_key_padding_mask=src_key_padding_mask, pos=pos) | |
| if self.norm is not None: | |
| x = self.norm(x) | |
| return x | |
| class SkipTransformerDecoder(nn.Module): | |
| def __init__(self, decoder_layer, num_layers, norm=None): | |
| super().__init__() | |
| self.d_model = decoder_layer.d_model | |
| self.num_layers = num_layers | |
| self.norm = norm | |
| assert num_layers % 2 == 1 | |
| num_block = (num_layers-1)//2 | |
| self.input_blocks = _get_clones(decoder_layer, num_block) | |
| self.middle_block = _get_clone(decoder_layer) | |
| self.output_blocks = _get_clones(decoder_layer, num_block) | |
| self.linear_blocks = _get_clones(nn.Linear(2*self.d_model, self.d_model), num_block) | |
| self._reset_parameters() | |
| def _reset_parameters(self): | |
| for p in self.parameters(): | |
| if p.dim() > 1: | |
| nn.init.xavier_uniform_(p) | |
| def forward(self, tgt, memory, | |
| tgt_mask: Optional[Tensor] = None, | |
| memory_mask: Optional[Tensor] = None, | |
| tgt_key_padding_mask: Optional[Tensor] = None, | |
| memory_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None, | |
| query_pos: Optional[Tensor] = None): | |
| x = tgt | |
| xs = [] | |
| for module in self.input_blocks: | |
| x = module(x, memory, tgt_mask=tgt_mask, | |
| memory_mask=memory_mask, | |
| tgt_key_padding_mask=tgt_key_padding_mask, | |
| memory_key_padding_mask=memory_key_padding_mask, | |
| pos=pos, query_pos=query_pos) | |
| xs.append(x) | |
| x = self.middle_block(x, memory, tgt_mask=tgt_mask, | |
| memory_mask=memory_mask, | |
| tgt_key_padding_mask=tgt_key_padding_mask, | |
| memory_key_padding_mask=memory_key_padding_mask, | |
| pos=pos, query_pos=query_pos) | |
| for (module, linear) in zip(self.output_blocks, self.linear_blocks): | |
| x = torch.cat([x, xs.pop()], dim=-1) | |
| x = linear(x) | |
| x = module(x, memory, tgt_mask=tgt_mask, | |
| memory_mask=memory_mask, | |
| tgt_key_padding_mask=tgt_key_padding_mask, | |
| memory_key_padding_mask=memory_key_padding_mask, | |
| pos=pos, query_pos=query_pos) | |
| if self.norm is not None: | |
| x = self.norm(x) | |
| return x | |
| class Transformer(nn.Module): | |
| def __init__(self, d_model=512, nhead=8, num_encoder_layers=6, | |
| num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, | |
| activation="relu", normalize_before=False, | |
| return_intermediate_dec=False): | |
| super().__init__() | |
| encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, | |
| dropout, activation, normalize_before) | |
| encoder_norm = nn.LayerNorm(d_model) if normalize_before else None | |
| self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) | |
| decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, | |
| dropout, activation, normalize_before) | |
| decoder_norm = nn.LayerNorm(d_model) | |
| self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm, | |
| return_intermediate=return_intermediate_dec) | |
| self._reset_parameters() | |
| self.d_model = d_model | |
| self.nhead = nhead | |
| def _reset_parameters(self): | |
| for p in self.parameters(): | |
| if p.dim() > 1: | |
| nn.init.xavier_uniform_(p) | |
| def forward(self, src, mask, query_embed, pos_embed): | |
| # flatten NxCxHxW to HWxNxC | |
| bs, c, h, w = src.shape | |
| src = src.flatten(2).permute(2, 0, 1) | |
| pos_embed = pos_embed.flatten(2).permute(2, 0, 1) | |
| query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) | |
| mask = mask.flatten(1) | |
| tgt = torch.zeros_like(query_embed) | |
| memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) | |
| hs = self.decoder(tgt, memory, memory_key_padding_mask=mask, | |
| pos=pos_embed, query_pos=query_embed) | |
| return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w) | |
| class TransformerEncoder(nn.Module): | |
| def __init__(self, encoder_layer, num_layers, norm=None): | |
| super().__init__() | |
| self.layers = _get_clones(encoder_layer, num_layers) | |
| self.num_layers = num_layers | |
| self.norm = norm | |
| def forward(self, src, | |
| mask: Optional[Tensor] = None, | |
| src_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None): | |
| output = src | |
| for layer in self.layers: | |
| output = layer(output, src_mask=mask, | |
| src_key_padding_mask=src_key_padding_mask, pos=pos) | |
| if self.norm is not None: | |
| output = self.norm(output) | |
| return output | |
| class TransformerDecoder(nn.Module): | |
| def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): | |
| super().__init__() | |
| self.layers = _get_clones(decoder_layer, num_layers) | |
| self.num_layers = num_layers | |
| self.norm = norm | |
| self.return_intermediate = return_intermediate | |
| def forward(self, tgt, memory, | |
| tgt_mask: Optional[Tensor] = None, | |
| memory_mask: Optional[Tensor] = None, | |
| tgt_key_padding_mask: Optional[Tensor] = None, | |
| memory_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None, | |
| query_pos: Optional[Tensor] = None): | |
| output = tgt | |
| intermediate = [] | |
| for layer in self.layers: | |
| output = layer(output, memory, tgt_mask=tgt_mask, | |
| memory_mask=memory_mask, | |
| tgt_key_padding_mask=tgt_key_padding_mask, | |
| memory_key_padding_mask=memory_key_padding_mask, | |
| pos=pos, query_pos=query_pos) | |
| if self.return_intermediate: | |
| intermediate.append(self.norm(output)) | |
| if self.norm is not None: | |
| output = self.norm(output) | |
| if self.return_intermediate: | |
| intermediate.pop() | |
| intermediate.append(output) | |
| if self.return_intermediate: | |
| return torch.stack(intermediate) | |
| return output.unsqueeze(0) | |
| class TransformerEncoderLayer(nn.Module): | |
| def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, | |
| activation="relu", normalize_before=False): | |
| super().__init__() | |
| self.d_model = d_model | |
| self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) | |
| # Implementation of Feedforward model | |
| self.linear1 = nn.Linear(d_model, dim_feedforward) | |
| self.dropout = nn.Dropout(dropout) | |
| self.linear2 = nn.Linear(dim_feedforward, d_model) | |
| self.norm1 = nn.LayerNorm(d_model) | |
| self.norm2 = nn.LayerNorm(d_model) | |
| self.dropout1 = nn.Dropout(dropout) | |
| self.dropout2 = nn.Dropout(dropout) | |
| self.activation = _get_activation_fn(activation) | |
| self.normalize_before = normalize_before | |
| def with_pos_embed(self, tensor, pos: Optional[Tensor]): | |
| return tensor if pos is None else tensor + pos | |
| def forward_post(self, | |
| src, | |
| src_mask: Optional[Tensor] = None, | |
| src_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None): | |
| q = k = self.with_pos_embed(src, pos) | |
| src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, | |
| key_padding_mask=src_key_padding_mask)[0] | |
| src = src + self.dropout1(src2) | |
| src = self.norm1(src) | |
| src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) | |
| src = src + self.dropout2(src2) | |
| src = self.norm2(src) | |
| return src | |
| def forward_pre(self, src, | |
| src_mask: Optional[Tensor] = None, | |
| src_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None): | |
| src2 = self.norm1(src) | |
| q = k = self.with_pos_embed(src2, pos) | |
| src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, | |
| key_padding_mask=src_key_padding_mask)[0] | |
| src = src + self.dropout1(src2) | |
| src2 = self.norm2(src) | |
| src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) | |
| src = src + self.dropout2(src2) | |
| return src | |
| def forward(self, src, | |
| src_mask: Optional[Tensor] = None, | |
| src_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None): | |
| if self.normalize_before: | |
| return self.forward_pre(src, src_mask, src_key_padding_mask, pos) | |
| return self.forward_post(src, src_mask, src_key_padding_mask, pos) | |
| class TransformerDecoderLayer(nn.Module): | |
| def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, | |
| activation="relu", normalize_before=False): | |
| super().__init__() | |
| self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) | |
| self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) | |
| # Implementation of Feedforward model | |
| self.d_model = d_model | |
| self.linear1 = nn.Linear(d_model, dim_feedforward) | |
| self.dropout = nn.Dropout(dropout) | |
| self.linear2 = nn.Linear(dim_feedforward, d_model) | |
| self.norm1 = nn.LayerNorm(d_model) | |
| self.norm2 = nn.LayerNorm(d_model) | |
| self.norm3 = nn.LayerNorm(d_model) | |
| self.dropout1 = nn.Dropout(dropout) | |
| self.dropout2 = nn.Dropout(dropout) | |
| self.dropout3 = nn.Dropout(dropout) | |
| self.activation = _get_activation_fn(activation) | |
| self.normalize_before = normalize_before | |
| def with_pos_embed(self, tensor, pos: Optional[Tensor]): | |
| return tensor if pos is None else tensor + pos | |
| def forward_post(self, tgt, memory, | |
| tgt_mask: Optional[Tensor] = None, | |
| memory_mask: Optional[Tensor] = None, | |
| tgt_key_padding_mask: Optional[Tensor] = None, | |
| memory_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None, | |
| query_pos: Optional[Tensor] = None): | |
| q = k = self.with_pos_embed(tgt, query_pos) | |
| tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, | |
| key_padding_mask=tgt_key_padding_mask)[0] | |
| tgt = tgt + self.dropout1(tgt2) | |
| tgt = self.norm1(tgt) | |
| tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), | |
| key=self.with_pos_embed(memory, pos), | |
| value=memory, attn_mask=memory_mask, | |
| key_padding_mask=memory_key_padding_mask)[0] | |
| tgt = tgt + self.dropout2(tgt2) | |
| tgt = self.norm2(tgt) | |
| tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) | |
| tgt = tgt + self.dropout3(tgt2) | |
| tgt = self.norm3(tgt) | |
| return tgt | |
| def forward_pre(self, tgt, memory, | |
| tgt_mask: Optional[Tensor] = None, | |
| memory_mask: Optional[Tensor] = None, | |
| tgt_key_padding_mask: Optional[Tensor] = None, | |
| memory_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None, | |
| query_pos: Optional[Tensor] = None): | |
| tgt2 = self.norm1(tgt) | |
| q = k = self.with_pos_embed(tgt2, query_pos) | |
| tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, | |
| key_padding_mask=tgt_key_padding_mask)[0] | |
| tgt = tgt + self.dropout1(tgt2) | |
| tgt2 = self.norm2(tgt) | |
| tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), | |
| key=self.with_pos_embed(memory, pos), | |
| value=memory, attn_mask=memory_mask, | |
| key_padding_mask=memory_key_padding_mask)[0] | |
| tgt = tgt + self.dropout2(tgt2) | |
| tgt2 = self.norm3(tgt) | |
| tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) | |
| tgt = tgt + self.dropout3(tgt2) | |
| return tgt | |
| def forward(self, tgt, memory, | |
| tgt_mask: Optional[Tensor] = None, | |
| memory_mask: Optional[Tensor] = None, | |
| tgt_key_padding_mask: Optional[Tensor] = None, | |
| memory_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None, | |
| query_pos: Optional[Tensor] = None): | |
| if self.normalize_before: | |
| return self.forward_pre(tgt, memory, tgt_mask, memory_mask, | |
| tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) | |
| return self.forward_post(tgt, memory, tgt_mask, memory_mask, | |
| tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) | |
| def _get_clone(module): | |
| return copy.deepcopy(module) | |
| def _get_clones(module, N): | |
| return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) | |
| def build_transformer(args): | |
| return Transformer( | |
| d_model=args.hidden_dim, | |
| dropout=args.dropout, | |
| nhead=args.nheads, | |
| dim_feedforward=args.dim_feedforward, | |
| num_encoder_layers=args.enc_layers, | |
| num_decoder_layers=args.dec_layers, | |
| normalize_before=args.pre_norm, | |
| return_intermediate_dec=True, | |
| ) | |
| def _get_activation_fn(activation): | |
| """Return an activation function given a string""" | |
| if activation == "relu": | |
| return F.relu | |
| if activation == "gelu": | |
| return F.gelu | |
| if activation == "glu": | |
| return F.glu | |
| raise RuntimeError(F"activation should be relu/gelu, not {activation}.") |