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| import copy | |
| import pdb | |
| from typing import Optional | |
| import torch | |
| import torch.nn.functional as F | |
| from torch import nn, Tensor | |
| def mask_logits(inputs, mask, mask_value=-1e30): | |
| mask = mask.type(torch.float32) | |
| return inputs + (1.0 - mask) * mask_value | |
| class Transformer(nn.Module): | |
| def __init__(self, d_model=512, nhead=8, num_encoder_layers=4, | |
| num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, droppath=0.1, | |
| activation="gelu", normalize_before=False, # False as default | |
| return_intermediate_dec=False): | |
| super().__init__() | |
| encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, | |
| dropout, droppath, activation, normalize_before) | |
| encoder_norm = nn.LayerNorm(d_model) if normalize_before else None | |
| self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_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, mask, pos_embed): | |
| """ | |
| Args: | |
| src: (batch_size, L, d) | |
| mask: (batch_size, L) | |
| query_embed: (#queries, d) -> my imple (batch_size, d) and #queries=1 | |
| pos_embed: (batch_size, L, d) the same as src | |
| Returns: | |
| """ | |
| # flatten NxCxHxW to HWxNxC | |
| src = src.permute(1, 0, 2) # (L, batch_size, d) | |
| pos_embed = pos_embed.permute(1, 0, 2) # (L, batch_size, d) | |
| memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) | |
| memory = memory.transpose(0, 1) | |
| return memory | |
| class TransformerEncoder(nn.Module): | |
| def __init__(self, encoder_layer, num_layers, norm=None, return_intermediate=False): | |
| super().__init__() | |
| self.layers = _get_clones(encoder_layer, num_layers) | |
| self.num_layers = num_layers | |
| self.norm = norm | |
| self.return_intermediate = return_intermediate | |
| def forward(self, src, | |
| mask: Optional[Tensor] = None, | |
| src_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None): | |
| output = src | |
| intermediate = [] | |
| for layer in self.layers: | |
| output = layer(output, src_mask=mask, | |
| src_key_padding_mask=src_key_padding_mask, pos=pos) | |
| if self.return_intermediate: | |
| intermediate.append(output) | |
| if self.norm is not None: | |
| output = self.norm(output) | |
| if self.return_intermediate: | |
| return torch.stack(intermediate) | |
| return output | |
| class TransformerEncoderLayer(nn.Module): | |
| def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, droppath=0.1, | |
| activation="relu", normalize_before=False): | |
| super().__init__() | |
| 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.droppath1 = DropPath(droppath) | |
| self.droppath2 = DropPath(droppath) | |
| 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] | |
| # src2 = self.self_attn_eff(q=q, k=k, v=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] | |
| src = src + self.droppath1(src2) | |
| src = self.norm1(src) | |
| src2 = self.linear2(self.activation(self.linear1(src))) | |
| # src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) | |
| src = src + self.droppath2(src2) | |
| src = self.norm2(src) | |
| 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) | |
| 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, | |
| droppath=args.droppath, | |
| 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 drop_path(x, drop_prob=0.0, training=False): | |
| """ | |
| Stochastic Depth per sample. | |
| """ | |
| if drop_prob == 0.0 or not training: | |
| return x | |
| keep_prob = 1 - drop_prob | |
| shape = (x.shape[0],) + (1,) * (x.ndim - 1) | |
| mask = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) | |
| mask.floor_() | |
| x = x.div(keep_prob) * mask | |
| return x | |
| class DropPath(nn.Module): | |
| """ | |
| Drop paths per sample (when applied in main path of residual blocks). | |
| """ | |
| def __init__(self, drop_prob=None): | |
| super(DropPath, self).__init__() | |
| self.drop_prob = drop_prob | |
| def forward(self, x): | |
| x = x.permute(1, 0, 2) | |
| res = drop_path(x, self.drop_prob, self.training) | |
| return res.permute(1, 0, 2) | |
| # return drop_path(x, self.drop_prob, self.training) | |
| 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}.") |