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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : msdeformattn.py
@Time : 2022/10/02 16:51:09
@Author : BQH
@Version : 1.0
@Contact : [email protected]
@License : (C)Copyright 2017-2018, Liugroup-NLPR-CASIA
@Desc : 修改自Mask2former,移除detectron2依赖
'''
# here put the import lib
import numpy as np
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from torch.nn import functional as F
from ..transformer_decoder.position_encoding import PositionEmbeddingSine
from ..transformer_decoder.transformer import _get_clones, _get_activation_fn
from .ops.modules import MSDeformAttn
# MSDeformAttn Transformer encoder in deformable detr
class MSDeformAttnTransformerEncoderLayer(nn.Module):
def __init__(self,
d_model=256, d_ffn=1024,
dropout=0.1, activation="relu",
n_levels=4, n_heads=8, n_points=4):
super().__init__()
# self attention
self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)
self.dropout1 = nn.Dropout(dropout)
self.norm1 = nn.LayerNorm(d_model)
# ffn
self.linear1 = nn.Linear(d_model, d_ffn)
self.activation = _get_activation_fn(activation)
self.dropout2 = nn.Dropout(dropout)
self.linear2 = nn.Linear(d_ffn, d_model)
self.dropout3 = nn.Dropout(dropout)
self.norm2 = nn.LayerNorm(d_model)
@staticmethod
def with_pos_embed(tensor, pos):
return tensor if pos is None else tensor + pos
def forward_ffn(self, src):
src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
src = src + self.dropout3(src2)
src = self.norm2(src)
return src
def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):
# self attention
src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask)
src = src + self.dropout1(src2)
src = self.norm1(src)
# ffn
src = self.forward_ffn(src)
return src
class MSDeformAttnTransformerEncoder(nn.Module):
def __init__(self, encoder_layer, num_layers):
super().__init__()
self.layers = _get_clones(encoder_layer, num_layers)
self.num_layers = num_layers
@staticmethod
def get_reference_points(spatial_shapes, valid_ratios, device):
reference_points_list = []
for lvl, (H_, W_) in enumerate(spatial_shapes):
ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))
ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
ref = torch.stack((ref_x, ref_y), -1) # [1, H_ * W_, 2]
reference_points_list.append(ref)
reference_points = torch.cat(reference_points_list, 1)
reference_points = reference_points[:, :, None] * valid_ratios[:, None]
return reference_points
def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):
output = src
reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)
for _, layer in enumerate(self.layers):
output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)
return output
class MSDeformAttnTransformerEncoderOnly(nn.Module):
def __init__(self, d_model=256, nhead=8,
num_encoder_layers=6, dim_feedforward=1024, dropout=0.1,
activation="relu",
num_feature_levels=4, enc_n_points=4,
):
super().__init__()
self.d_model = d_model
self.nhead = nhead
encoder_layer = MSDeformAttnTransformerEncoderLayer(d_model, dim_feedforward,
dropout, activation,
num_feature_levels, nhead, enc_n_points)
self.encoder = MSDeformAttnTransformerEncoder(encoder_layer, num_encoder_layers)
self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
self._reset_parameters()
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
for m in self.modules():
if isinstance(m, MSDeformAttn):
m._reset_parameters()
nn.init.normal_(self.level_embed)
def get_valid_ratio(self, mask):
_, H, W = mask.shape
valid_H = torch.sum(~mask[:, :, 0], 1)
valid_W = torch.sum(~mask[:, 0, :], 1)
valid_ratio_h = valid_H.float() / H
valid_ratio_w = valid_W.float() / W
valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
return valid_ratio
def forward(self, srcs, pos_embeds):
masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in srcs]
# prepare input for encoder
src_flatten = []
mask_flatten = []
lvl_pos_embed_flatten = []
spatial_shapes = []
for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
bs, c, h, w = src.shape
spatial_shape = (h, w)
spatial_shapes.append(spatial_shape)
src = src.flatten(2).transpose(1, 2)
mask = mask.flatten(1)
pos_embed = pos_embed.flatten(2).transpose(1, 2)
lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
lvl_pos_embed_flatten.append(lvl_pos_embed)
src_flatten.append(src)
mask_flatten.append(mask)
src_flatten = torch.cat(src_flatten, 1)
mask_flatten = torch.cat(mask_flatten, 1)
lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)
level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))
valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
# encoder
memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten)
return memory, spatial_shapes, level_start_index
class MSDeformAttnPixelDecoder(nn.Module):
def __init__(
self,
input_shape,
transformer_dropout=0.1,
transformer_nheads=8,
transformer_dim_feedforward=2048,
transformer_enc_layers=6,
conv_dim=256,
mask_dim=256,
# deformable transformer encoder args
transformer_in_features= ["res3", "res4", "res5"],
common_stride=4,
):
super().__init__()
# backbone中["res3", "res4", "res5"]特征层的(channel, stride), eg. [(32,4), (64, 8),(128, 16),(256, 32)]
transformer_input_shape = {k: v for k, v in input_shape.items() if k in transformer_in_features}
# this is the input shape of pixel decoder
self.in_features = [k for k, v in input_shape.items()] # starting from "res3" to "res5"
self.feature_channels = [v.channel for k, v in input_shape.items()] # eg. [16, 64, 128, 256]
# this is the input shape of transformer encoder (could use less features than pixel decoder
self.transformer_in_features = [k for k, v in transformer_input_shape.items()] # starting from "res3" to "res5"
transformer_in_channels = [v.channel for k, v in transformer_input_shape.items()] # eg. [64, 128, 256]
self.transformer_feature_strides = [v.stride for k, v in transformer_input_shape.items()] # to decide extra FPN layers
self.transformer_num_feature_levels = len(self.transformer_in_features)
if self.transformer_num_feature_levels > 1:
input_proj_list = []
# from low resolution to high resolution (res5 -> res3)
for in_channels in transformer_in_channels[::-1]:
input_proj_list.append(nn.Sequential(
nn.Conv2d(in_channels, conv_dim, kernel_size=1),
nn.GroupNorm(32, conv_dim),
))
self.input_proj = nn.ModuleList(input_proj_list)
else:
self.input_proj = nn.ModuleList([
nn.Sequential(
nn.Conv2d(transformer_in_channels[-1], conv_dim, kernel_size=1),
nn.GroupNorm(32, conv_dim),
)])
for proj in self.input_proj:
nn.init.xavier_uniform_(proj[0].weight, gain=1)
nn.init.constant_(proj[0].bias, 0)
self.transformer = MSDeformAttnTransformerEncoderOnly(
d_model=conv_dim,
dropout=transformer_dropout,
nhead=transformer_nheads,
dim_feedforward=transformer_dim_feedforward,
num_encoder_layers=transformer_enc_layers,
num_feature_levels=self.transformer_num_feature_levels,
)
N_steps = conv_dim // 2
self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)
self.mask_dim = mask_dim
# use 1x1 conv instead
self.mask_features = nn.Conv2d(
conv_dim,
mask_dim,
kernel_size=1,
stride=1,
padding=0,
)
weight_init.c2_xavier_fill(self.mask_features)
self.maskformer_num_feature_levels = 3 # always use 3 scales
self.common_stride = common_stride
# extra fpn levels
stride = min(self.transformer_feature_strides)
self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride))
lateral_convs = []
output_convs = []
for idx, in_channels in enumerate(self.feature_channels[:self.num_fpn_levels]): # res2 -> fpn
lateral_conv = nn.Sequential(nn.Conv2d(in_channels, conv_dim, kernel_size=1),
nn.GroupNorm(32, conv_dim),
nn.ReLU(inplace=True))
output_conv = nn.Sequential(nn.Conv2d(conv_dim, conv_dim, kernel_size=3, stride=1, padding=1),
nn.GroupNorm(32, conv_dim),
nn.ReLU(inplace=True))
weight_init.c2_xavier_fill(lateral_conv[0])
weight_init.c2_xavier_fill(output_conv[0])
self.add_module("adapter_{}".format(idx + 1), lateral_conv)
self.add_module("layer_{}".format(idx + 1), output_conv)
lateral_convs.append(lateral_conv)
output_convs.append(output_conv)
# Place convs into top-down order (from low to high resolution)
# to make the top-down computation in forward clearer.
self.lateral_convs = lateral_convs[::-1]
self.output_convs = output_convs[::-1]
def forward_features(self, features):
srcs = []
pos = []
# Reverse feature maps into top-down order (from low to high resolution), 'res5' -> 'res3'
for idx, f in enumerate(self.transformer_in_features[::-1]):
x = features[f].float() # deformable detr does not support half precision
srcs.append(self.input_proj[idx](x))
pos.append(self.pe_layer(x))
y, spatial_shapes, level_start_index = self.transformer(srcs, pos)
bs = y.shape[0]
split_size_or_sections = [None] * self.transformer_num_feature_levels
for i in range(self.transformer_num_feature_levels):
if i < self.transformer_num_feature_levels - 1:
split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i]
else:
split_size_or_sections[i] = y.shape[1] - level_start_index[i]
y = torch.split(y, split_size_or_sections, dim=1)
out = []
multi_scale_features = []
num_cur_levels = 0
for i, z in enumerate(y):
out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1]))
# append `out` with extra FPN levels
# Reverse feature maps into top-down order (from low to high resolution)
for idx, f in enumerate(self.in_features[:self.num_fpn_levels][::-1]):
x = features[f].float()
lateral_conv = self.lateral_convs[idx]
output_conv = self.output_convs[idx]
cur_fpn = lateral_conv(x)
# Following FPN implementation, we use nearest upsampling here
y = cur_fpn + F.interpolate(out[-1], size=cur_fpn.shape[-2:], mode="bilinear", align_corners=False)
y = output_conv(y)
out.append(y)
for o in out:
if num_cur_levels < self.maskformer_num_feature_levels:
multi_scale_features.append(o)
num_cur_levels += 1
return self.mask_features(out[-1]), out[0], multi_scale_features
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