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transformer_encoder.py
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transformer_encoder.py
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"""
Code for the encoder of TransPoseNet
code is based on https://github.com/facebookresearch/detr/tree/master/models
(transformer + position encoding. Note: LN at the end of the encoder is not removed)
with the following modifications:
- decoder is removed
- encoder is changed to take the encoding of the pose token and to output just the token
"""
import copy
from typing import Optional
import torch
import torch.nn.functional as F
from torch import nn, Tensor
class Transformer(nn.Module):
default_config = {
"hidden_dim":512,
"nhead":8,
"num_encoder_layers": 6,
"dim_feedforward": 2048,
"dropout":0.1,
"activation": "gelu",
"normalize_before": True,
"return_intermediate_dec": False
}
def __init__(self, config = {}):
super().__init__()
config = {**self.default_config, **config}
d_model = config.get("hidden_dim")
nhead = config.get("nhead")
dim_feedforward = config.get("dim_feedforward")
dropout = config.get("dropout")
activation = config.get("activation")
normalize_before = config.get("normalize_before")
num_encoder_layers = config.get("num_encoder_layers")
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)
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, pose_token_embed):
# flatten NxCxHxW to HWxNxC
bs, c, h, w = src.shape
pose_pos_embed, activation_pos_embed = pos_embed
activation_pos_embed = activation_pos_embed.flatten(2).permute(2, 0, 1)
pose_pos_embed = pose_pos_embed.unsqueeze(2).permute(2, 0, 1)
pos_embed = torch.cat([pose_pos_embed, activation_pos_embed])
src = src.flatten(2).permute(2, 0, 1)
pose_token_embed = pose_token_embed.unsqueeze(1).repeat(1, bs, 1)
src = torch.cat([pose_token_embed, src])
memory = self.encoder(src, src_key_padding_mask=None, pos=pos_embed)
return memory.transpose(0,1)
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 TransformerEncoderLayer(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)
# 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)
def _get_clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
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}.")
def build_transformer(config):
return Transformer(config)