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# Copyright (c) Facebook, Inc. and its affiliates. | |
import logging | |
import numpy as np | |
from typing import Callable, Dict, List, Optional, Tuple, Union | |
import fvcore.nn.weight_init as weight_init | |
import torch | |
from torch import nn | |
from torch.nn import functional as F | |
from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_ | |
from torch.cuda.amp import autocast | |
from detectron2.config import configurable | |
from detectron2.layers import Conv2d, ShapeSpec, get_norm | |
from detectron2.modeling import SEM_SEG_HEADS_REGISTRY | |
from ..transformer_decoder.position_encoding import PositionEmbeddingSine | |
from .ops.modules import MSDeformAttn | |
import copy | |
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_pixel_decoder(cfg, input_shape): | |
""" | |
Build a pixel decoder from `cfg.MODEL.ONE_FORMER.PIXEL_DECODER_NAME`. | |
""" | |
name = cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME | |
model = SEM_SEG_HEADS_REGISTRY.get(name)(cfg, input_shape) | |
forward_features = getattr(model, "forward_features", None) | |
if not callable(forward_features): | |
raise ValueError( | |
"Only SEM_SEG_HEADS with forward_features method can be used as pixel decoder. " | |
f"Please implement forward_features for {name} to only return mask features." | |
) | |
return model | |
# MSDeformAttn Transformer encoder in deformable detr | |
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() | |
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 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) | |
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 | |
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) | |
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 MSDeformAttnPixelDecoder(nn.Module): | |
def __init__( | |
self, | |
input_shape: Dict[str, ShapeSpec], | |
*, | |
transformer_dropout: float, | |
transformer_nheads: int, | |
transformer_dim_feedforward: int, | |
transformer_enc_layers: int, | |
conv_dim: int, | |
mask_dim: int, | |
norm: Optional[Union[str, Callable]] = None, | |
# deformable transformer encoder args | |
transformer_in_features: List[str], | |
common_stride: int, | |
): | |
""" | |
NOTE: this interface is experimental. | |
Args: | |
input_shape: shapes (channels and stride) of the input features | |
transformer_dropout: dropout probability in transformer | |
transformer_nheads: number of heads in transformer | |
transformer_dim_feedforward: dimension of feedforward network | |
transformer_enc_layers: number of transformer encoder layers | |
conv_dims: number of output channels for the intermediate conv layers. | |
mask_dim: number of output channels for the final conv layer. | |
norm (str or callable): normalization for all conv layers | |
""" | |
super().__init__() | |
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 | |
input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) | |
self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5" | |
self.feature_strides = [v.stride for k, v in input_shape] | |
self.feature_channels = [v.channels for k, v in input_shape] | |
# this is the input shape of transformer encoder (could use less features than pixel decoder | |
transformer_input_shape = sorted(transformer_input_shape.items(), key=lambda x: x[1].stride) | |
self.transformer_in_features = [k for k, v in transformer_input_shape] # starting from "res2" to "res5" | |
transformer_in_channels = [v.channels for k, v in transformer_input_shape] | |
self.transformer_feature_strides = [v.stride for k, v in transformer_input_shape] # 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 -> res2) | |
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 = 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 = [] | |
use_bias = norm == "" | |
for idx, in_channels in enumerate(self.feature_channels[:self.num_fpn_levels]): | |
lateral_norm = get_norm(norm, conv_dim) | |
output_norm = get_norm(norm, conv_dim) | |
lateral_conv = Conv2d( | |
in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm | |
) | |
output_conv = Conv2d( | |
conv_dim, | |
conv_dim, | |
kernel_size=3, | |
stride=1, | |
padding=1, | |
bias=use_bias, | |
norm=output_norm, | |
activation=F.relu, | |
) | |
weight_init.c2_xavier_fill(lateral_conv) | |
weight_init.c2_xavier_fill(output_conv) | |
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 from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): | |
ret = {} | |
ret["input_shape"] = { | |
k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES | |
} | |
ret["conv_dim"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM | |
ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM | |
ret["norm"] = cfg.MODEL.SEM_SEG_HEAD.NORM | |
ret["transformer_dropout"] = cfg.MODEL.MASK_FORMER.DROPOUT | |
ret["transformer_nheads"] = cfg.MODEL.MASK_FORMER.NHEADS | |
# ret["transformer_dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD | |
ret["transformer_dim_feedforward"] = 1024 # use 1024 for deformable transformer encoder | |
ret[ | |
"transformer_enc_layers" | |
] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config | |
ret["transformer_in_features"] = cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES | |
ret["common_stride"] = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE | |
return ret | |
def forward_features(self, features): | |
srcs = [] | |
pos = [] | |
# Reverse feature maps into top-down order (from low to high resolution) | |
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 | |