pixel_decoder.py
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# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import PLUGIN_LAYERS, Conv2d, ConvModule, caffe2_xavier_init
from mmcv.cnn.bricks.transformer import (build_positional_encoding,
build_transformer_layer_sequence)
from mmcv.runner import BaseModule, ModuleList
@PLUGIN_LAYERS.register_module()
class PixelDecoder(BaseModule):
"""Pixel decoder with a structure like fpn.
Args:
in_channels (list[int] | tuple[int]): Number of channels in the
input feature maps.
feat_channels (int): Number channels for feature.
out_channels (int): Number channels for output.
norm_cfg (:obj:`mmcv.ConfigDict` | dict): Config for normalization.
Defaults to dict(type='GN', num_groups=32).
act_cfg (:obj:`mmcv.ConfigDict` | dict): Config for activation.
Defaults to dict(type='ReLU').
encoder (:obj:`mmcv.ConfigDict` | dict): Config for transorformer
encoder.Defaults to None.
positional_encoding (:obj:`mmcv.ConfigDict` | dict): Config for
transformer encoder position encoding. Defaults to
dict(type='SinePositionalEncoding', num_feats=128,
normalize=True).
init_cfg (:obj:`mmcv.ConfigDict` | dict): Initialization config dict.
Default: None
"""
def __init__(self,
in_channels,
feat_channels,
out_channels,
norm_cfg=dict(type='GN', num_groups=32),
act_cfg=dict(type='ReLU'),
init_cfg=None):
super().__init__(init_cfg=init_cfg)
self.in_channels = in_channels
self.num_inputs = len(in_channels)
self.lateral_convs = ModuleList()
self.output_convs = ModuleList()
self.use_bias = norm_cfg is None
for i in range(0, self.num_inputs - 1):
lateral_conv = ConvModule(
in_channels[i],
feat_channels,
kernel_size=1,
bias=self.use_bias,
norm_cfg=norm_cfg,
act_cfg=None)
output_conv = ConvModule(
feat_channels,
feat_channels,
kernel_size=3,
stride=1,
padding=1,
bias=self.use_bias,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
self.lateral_convs.append(lateral_conv)
self.output_convs.append(output_conv)
self.last_feat_conv = ConvModule(
in_channels[-1],
feat_channels,
kernel_size=3,
padding=1,
stride=1,
bias=self.use_bias,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
self.mask_feature = Conv2d(
feat_channels, out_channels, kernel_size=3, stride=1, padding=1)
def init_weights(self):
"""Initialize weights."""
for i in range(0, self.num_inputs - 2):
caffe2_xavier_init(self.lateral_convs[i].conv, bias=0)
caffe2_xavier_init(self.output_convs[i].conv, bias=0)
caffe2_xavier_init(self.mask_feature, bias=0)
caffe2_xavier_init(self.last_feat_conv, bias=0)
def forward(self, feats, img_metas):
"""
Args:
feats (list[Tensor]): Feature maps of each level. Each has
shape of (batch_size, c, h, w).
img_metas (list[dict]): List of image information. Pass in
for creating more accurate padding mask. Not used here.
Returns:
tuple: a tuple containing the following:
- mask_feature (Tensor): Shape (batch_size, c, h, w).
- memory (Tensor): Output of last stage of backbone.\
Shape (batch_size, c, h, w).
"""
y = self.last_feat_conv(feats[-1])
for i in range(self.num_inputs - 2, -1, -1):
x = feats[i]
cur_feat = self.lateral_convs[i](x)
y = cur_feat + \
F.interpolate(y, size=cur_feat.shape[-2:], mode='nearest')
y = self.output_convs[i](y)
mask_feature = self.mask_feature(y)
memory = feats[-1]
return mask_feature, memory
@PLUGIN_LAYERS.register_module()
class TransformerEncoderPixelDecoder(PixelDecoder):
"""Pixel decoder with transormer encoder inside.
Args:
in_channels (list[int] | tuple[int]): Number of channels in the
input feature maps.
feat_channels (int): Number channels for feature.
out_channels (int): Number channels for output.
norm_cfg (:obj:`mmcv.ConfigDict` | dict): Config for normalization.
Defaults to dict(type='GN', num_groups=32).
act_cfg (:obj:`mmcv.ConfigDict` | dict): Config for activation.
Defaults to dict(type='ReLU').
encoder (:obj:`mmcv.ConfigDict` | dict): Config for transorformer
encoder.Defaults to None.
positional_encoding (:obj:`mmcv.ConfigDict` | dict): Config for
transformer encoder position encoding. Defaults to
dict(type='SinePositionalEncoding', num_feats=128,
normalize=True).
init_cfg (:obj:`mmcv.ConfigDict` | dict): Initialization config dict.
Default: None
"""
def __init__(self,
in_channels,
feat_channels,
out_channels,
norm_cfg=dict(type='GN', num_groups=32),
act_cfg=dict(type='ReLU'),
encoder=None,
positional_encoding=dict(
type='SinePositionalEncoding',
num_feats=128,
normalize=True),
init_cfg=None):
super(TransformerEncoderPixelDecoder, self).__init__(
in_channels,
feat_channels,
out_channels,
norm_cfg,
act_cfg,
init_cfg=init_cfg)
self.last_feat_conv = None
self.encoder = build_transformer_layer_sequence(encoder)
self.encoder_embed_dims = self.encoder.embed_dims
assert self.encoder_embed_dims == feat_channels, 'embed_dims({}) of ' \
'tranformer encoder must equal to feat_channels({})'.format(
feat_channels, self.encoder_embed_dims)
self.positional_encoding = build_positional_encoding(
positional_encoding)
self.encoder_in_proj = Conv2d(
in_channels[-1], feat_channels, kernel_size=1)
self.encoder_out_proj = ConvModule(
feat_channels,
feat_channels,
kernel_size=3,
stride=1,
padding=1,
bias=self.use_bias,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
def init_weights(self):
"""Initialize weights."""
for i in range(0, self.num_inputs - 2):
caffe2_xavier_init(self.lateral_convs[i].conv, bias=0)
caffe2_xavier_init(self.output_convs[i].conv, bias=0)
caffe2_xavier_init(self.mask_feature, bias=0)
caffe2_xavier_init(self.encoder_in_proj, bias=0)
caffe2_xavier_init(self.encoder_out_proj.conv, bias=0)
for p in self.encoder.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(self, feats, img_metas):
"""
Args:
feats (list[Tensor]): Feature maps of each level. Each has
shape of (batch_size, c, h, w).
img_metas (list[dict]): List of image information. Pass in
for creating more accurate padding mask.
Returns:
tuple: a tuple containing the following:
- mask_feature (Tensor): shape (batch_size, c, h, w).
- memory (Tensor): shape (batch_size, c, h, w).
"""
feat_last = feats[-1]
bs, c, h, w = feat_last.shape
input_img_h, input_img_w = img_metas[0]['batch_input_shape']
padding_mask = feat_last.new_ones((bs, input_img_h, input_img_w),
dtype=torch.float32)
for i in range(bs):
img_h, img_w, _ = img_metas[i]['img_shape']
padding_mask[i, :img_h, :img_w] = 0
padding_mask = F.interpolate(
padding_mask.unsqueeze(1),
size=feat_last.shape[-2:],
mode='nearest').to(torch.bool).squeeze(1)
pos_embed = self.positional_encoding(padding_mask)
feat_last = self.encoder_in_proj(feat_last)
# (batch_size, c, h, w) -> (num_queries, batch_size, c)
feat_last = feat_last.flatten(2).permute(2, 0, 1)
pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
# (batch_size, h, w) -> (batch_size, h*w)
padding_mask = padding_mask.flatten(1)
memory = self.encoder(
query=feat_last,
key=None,
value=None,
query_pos=pos_embed,
query_key_padding_mask=padding_mask)
# (num_queries, batch_size, c) -> (batch_size, c, h, w)
memory = memory.permute(1, 2, 0).view(bs, self.encoder_embed_dims, h,
w)
y = self.encoder_out_proj(memory)
for i in range(self.num_inputs - 2, -1, -1):
x = feats[i]
cur_feat = self.lateral_convs[i](x)
y = cur_feat + \
F.interpolate(y, size=cur_feat.shape[-2:], mode='nearest')
y = self.output_convs[i](y)
mask_feature = self.mask_feature(y)
return mask_feature, memory