pretrain/transformer.py

#most of the codes below are copied from Query2label and DETR
import torch
import torch.nn.functional as F
from torch import nn, Tensor
from torch.nn import MultiheadAttention
from typing import Optional, List
import copy
def _get_activation_fn(activation):
    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}.")
class TransformerEncoderLayer(nn.Module):
    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
                 activation="relu"):
        super().__init__()
        self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout)
        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)
    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
        return tensor if pos is None else tensor + pos
    def forward(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

class TransformerDecoderLayer(nn.Module):
    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
                 activation="relu"):
        super().__init__()
        self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout)
        self.multihead_attn = 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.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)
    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
        return tensor if pos is None else tensor + pos
    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):
        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 _get_clones(module, N):
    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
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
        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.norm is not None:
            output = self.norm(output)
        return output


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"
                 ):
        super().__init__()
        self.num_encoder_layers = num_encoder_layers
        if num_decoder_layers > 0:
            encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward,
                                                    dropout, activation)
            encoder_norm = nn.LayerNorm(d_model)
            self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)

        decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward,
                                                dropout, activation)
        decoder_norm = nn.LayerNorm(d_model)
        self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm)

        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, query_embed, pos_embed=None, mask=None):
        bs, c, w = src.shape
        src = src.permute(2, 0, 1)
        query_embed = query_embed.transpose(0,1)
            # .unsqueeze(1).repeat(1, bs, 1)
        if mask is not None:
            mask = mask.flatten(1)

        memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)
        hs = self.decoder(query_embed, memory, memory_key_padding_mask=mask,
                          pos=pos_embed, query_pos=None)
        return hs.transpose(0,1)
        # return hs.transpose(1, 2), memory[:h * w].permute(1, 2, 0).view(bs, c, h, w)