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head.py
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# ========================================
# Modified by Shoufa Chen
# ========================================
# Modified by Peize Sun, Rufeng Zhang
# Contact: {sunpeize, cxrfzhang}@foxmail.com
#
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
DiffusionDet Transformer class.
Copy-paste from torch.nn.Transformer with modifications:
* positional encodings are passed in MHattention
* extra LN at the end of encoder is removed
* decoder returns a stack of activations from all decoding layers
"""
import copy
import math
import numpy as np
import torch
from torch import nn, Tensor
import torch.nn.functional as F
from detectron2.modeling.poolers import ROIPooler
from detectron2.structures import Boxes
_DEFAULT_SCALE_CLAMP = math.log(100000.0 / 16)
class SinusoidalPositionEmbeddings(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, time):
device = time.device
half_dim = self.dim // 2
embeddings = math.log(10000) / (half_dim - 1)
embeddings = torch.exp(torch.arange(half_dim, device=device) * -embeddings)
embeddings = time[:, None] * embeddings[None, :]
embeddings = torch.cat((embeddings.sin(), embeddings.cos()), dim=-1)
return embeddings
class GaussianFourierProjection(nn.Module):
"""Gaussian random features for encoding time steps."""
def __init__(self, embed_dim, scale=30.):
super().__init__()
# Randomly sample weights during initialization. These weights are fixed
# during optimization and are not trainable.
self.W = nn.Parameter(torch.randn(embed_dim // 2) * scale, requires_grad=False)
def forward(self, x):
x_proj = x[:, None] * self.W[None, :] * 2 * np.pi
return torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)
class Dense(nn.Module):
"""A fully connected layer that reshapes outputs to feature maps."""
def __init__(self, input_dim, output_dim):
super().__init__()
self.dense = nn.Linear(input_dim, output_dim)
def forward(self, x):
return self.dense(x)
class DynamicHead(nn.Module):
def __init__(self, cfg, roi_input_shape):
super().__init__()
# Build RoI.
box_pooler = self._init_box_pooler(cfg, roi_input_shape)
self.box_pooler = box_pooler
# Build heads.
num_classes = cfg.MODEL.DiffusionDet.NUM_CLASSES
d_model = cfg.MODEL.DiffusionDet.HIDDEN_DIM
dim_feedforward = cfg.MODEL.DiffusionDet.DIM_FEEDFORWARD
nhead = cfg.MODEL.DiffusionDet.NHEADS
dropout = cfg.MODEL.DiffusionDet.DROPOUT
activation = cfg.MODEL.DiffusionDet.ACTIVATION
num_heads = cfg.MODEL.DiffusionDet.NUM_HEADS
rcnn_head = RCNNHead(cfg, d_model, num_classes, dim_feedforward, nhead, dropout, activation)
self.head_series = _get_clones(rcnn_head, num_heads)
self.num_heads = num_heads
self.return_intermediate = cfg.MODEL.DiffusionDet.DEEP_SUPERVISION
# Gaussian random feature embedding layer for time
self.d_model = d_model
time_dim = d_model * 4
self.time_mlp = nn.Sequential(
SinusoidalPositionEmbeddings(d_model),
nn.Linear(d_model, time_dim),
nn.GELU(),
nn.Linear(time_dim, time_dim),
)
# Init parameters.
self.use_focal = cfg.MODEL.DiffusionDet.USE_FOCAL
self.use_fed_loss = cfg.MODEL.DiffusionDet.USE_FED_LOSS
self.num_classes = num_classes
if self.use_focal or self.use_fed_loss:
prior_prob = cfg.MODEL.DiffusionDet.PRIOR_PROB
self.bias_value = -math.log((1 - prior_prob) / prior_prob)
self._reset_parameters()
def _reset_parameters(self):
# init all parameters.
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
# initialize the bias for focal loss and fed loss.
if self.use_focal or self.use_fed_loss:
if p.shape[-1] == self.num_classes or p.shape[-1] == self.num_classes + 1:
nn.init.constant_(p, self.bias_value)
@staticmethod
def _init_box_pooler(cfg, input_shape):
in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES
pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features)
sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE
# If StandardROIHeads is applied on multiple feature maps (as in FPN),
# then we share the same predictors and therefore the channel counts must be the same
in_channels = [input_shape[f].channels for f in in_features]
# Check all channel counts are equal
assert len(set(in_channels)) == 1, in_channels
box_pooler = ROIPooler(
output_size=pooler_resolution,
scales=pooler_scales,
sampling_ratio=sampling_ratio,
pooler_type=pooler_type,
)
return box_pooler
def forward(self, features, init_bboxes, t, init_features):
# assert t shape (batch_size)
time = self.time_mlp(t)
inter_class_logits = []
inter_pred_bboxes = []
bs = len(features[0])
bboxes = init_bboxes
num_boxes = bboxes.shape[1]
if init_features is not None:
init_features = init_features[None].repeat(1, bs, 1)
proposal_features = init_features.clone()
else:
proposal_features = None
for head_idx, rcnn_head in enumerate(self.head_series):
class_logits, pred_bboxes, proposal_features = rcnn_head(features, bboxes, proposal_features, self.box_pooler, time)
if self.return_intermediate:
inter_class_logits.append(class_logits)
inter_pred_bboxes.append(pred_bboxes)
bboxes = pred_bboxes.detach()
if self.return_intermediate:
return torch.stack(inter_class_logits), torch.stack(inter_pred_bboxes)
return class_logits[None], pred_bboxes[None]
class RCNNHead(nn.Module):
def __init__(self, cfg, d_model, num_classes, dim_feedforward=2048, nhead=8, dropout=0.1, activation="relu",
scale_clamp: float = _DEFAULT_SCALE_CLAMP, bbox_weights=(2.0, 2.0, 1.0, 1.0)):
super().__init__()
self.d_model = d_model
# dynamic.
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.inst_interact = DynamicConv(cfg)
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.norm3 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.dropout3 = nn.Dropout(dropout)
self.activation = _get_activation_fn(activation)
# block time mlp
self.block_time_mlp = nn.Sequential(nn.SiLU(), nn.Linear(d_model * 4, d_model * 2))
# cls.
num_cls = cfg.MODEL.DiffusionDet.NUM_CLS
cls_module = list()
for _ in range(num_cls):
cls_module.append(nn.Linear(d_model, d_model, False))
cls_module.append(nn.LayerNorm(d_model))
cls_module.append(nn.ReLU(inplace=True))
self.cls_module = nn.ModuleList(cls_module)
# reg.
num_reg = cfg.MODEL.DiffusionDet.NUM_REG
reg_module = list()
for _ in range(num_reg):
reg_module.append(nn.Linear(d_model, d_model, False))
reg_module.append(nn.LayerNorm(d_model))
reg_module.append(nn.ReLU(inplace=True))
self.reg_module = nn.ModuleList(reg_module)
# pred.
self.use_focal = cfg.MODEL.DiffusionDet.USE_FOCAL
self.use_fed_loss = cfg.MODEL.DiffusionDet.USE_FED_LOSS
if self.use_focal or self.use_fed_loss:
self.class_logits = nn.Linear(d_model, num_classes)
else:
self.class_logits = nn.Linear(d_model, num_classes + 1)
self.bboxes_delta = nn.Linear(d_model, 4)
self.scale_clamp = scale_clamp
self.bbox_weights = bbox_weights
def forward(self, features, bboxes, pro_features, pooler, time_emb):
"""
:param bboxes: (N, nr_boxes, 4)
:param pro_features: (N, nr_boxes, d_model)
"""
N, nr_boxes = bboxes.shape[:2]
# roi_feature.
proposal_boxes = list()
for b in range(N):
proposal_boxes.append(Boxes(bboxes[b]))
roi_features = pooler(features, proposal_boxes)
if pro_features is None:
pro_features = roi_features.view(N, nr_boxes, self.d_model, -1).mean(-1)
roi_features = roi_features.view(N * nr_boxes, self.d_model, -1).permute(2, 0, 1)
# self_att.
pro_features = pro_features.view(N, nr_boxes, self.d_model).permute(1, 0, 2)
pro_features2 = self.self_attn(pro_features, pro_features, value=pro_features)[0]
pro_features = pro_features + self.dropout1(pro_features2)
pro_features = self.norm1(pro_features)
# inst_interact.
pro_features = pro_features.view(nr_boxes, N, self.d_model).permute(1, 0, 2).reshape(1, N * nr_boxes, self.d_model)
pro_features2 = self.inst_interact(pro_features, roi_features)
pro_features = pro_features + self.dropout2(pro_features2)
obj_features = self.norm2(pro_features)
# obj_feature.
obj_features2 = self.linear2(self.dropout(self.activation(self.linear1(obj_features))))
obj_features = obj_features + self.dropout3(obj_features2)
obj_features = self.norm3(obj_features)
fc_feature = obj_features.transpose(0, 1).reshape(N * nr_boxes, -1)
scale_shift = self.block_time_mlp(time_emb)
scale_shift = torch.repeat_interleave(scale_shift, nr_boxes, dim=0)
scale, shift = scale_shift.chunk(2, dim=1)
fc_feature = fc_feature * (scale + 1) + shift
cls_feature = fc_feature.clone()
reg_feature = fc_feature.clone()
for cls_layer in self.cls_module:
cls_feature = cls_layer(cls_feature)
for reg_layer in self.reg_module:
reg_feature = reg_layer(reg_feature)
class_logits = self.class_logits(cls_feature)
bboxes_deltas = self.bboxes_delta(reg_feature)
pred_bboxes = self.apply_deltas(bboxes_deltas, bboxes.view(-1, 4))
return class_logits.view(N, nr_boxes, -1), pred_bboxes.view(N, nr_boxes, -1), obj_features
def apply_deltas(self, deltas, boxes):
"""
Apply transformation `deltas` (dx, dy, dw, dh) to `boxes`.
Args:
deltas (Tensor): transformation deltas of shape (N, k*4), where k >= 1.
deltas[i] represents k potentially different class-specific
box transformations for the single box boxes[i].
boxes (Tensor): boxes to transform, of shape (N, 4)
"""
boxes = boxes.to(deltas.dtype)
widths = boxes[:, 2] - boxes[:, 0]
heights = boxes[:, 3] - boxes[:, 1]
ctr_x = boxes[:, 0] + 0.5 * widths
ctr_y = boxes[:, 1] + 0.5 * heights
wx, wy, ww, wh = self.bbox_weights
dx = deltas[:, 0::4] / wx
dy = deltas[:, 1::4] / wy
dw = deltas[:, 2::4] / ww
dh = deltas[:, 3::4] / wh
# Prevent sending too large values into torch.exp()
dw = torch.clamp(dw, max=self.scale_clamp)
dh = torch.clamp(dh, max=self.scale_clamp)
pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
pred_w = torch.exp(dw) * widths[:, None]
pred_h = torch.exp(dh) * heights[:, None]
pred_boxes = torch.zeros_like(deltas)
pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w # x1
pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h # y1
pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w # x2
pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h # y2
return pred_boxes
class DynamicConv(nn.Module):
def __init__(self, cfg):
super().__init__()
self.hidden_dim = cfg.MODEL.DiffusionDet.HIDDEN_DIM
self.dim_dynamic = cfg.MODEL.DiffusionDet.DIM_DYNAMIC
self.num_dynamic = cfg.MODEL.DiffusionDet.NUM_DYNAMIC
self.num_params = self.hidden_dim * self.dim_dynamic
self.dynamic_layer = nn.Linear(self.hidden_dim, self.num_dynamic * self.num_params)
self.norm1 = nn.LayerNorm(self.dim_dynamic)
self.norm2 = nn.LayerNorm(self.hidden_dim)
self.activation = nn.ReLU(inplace=True)
pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
num_output = self.hidden_dim * pooler_resolution ** 2
self.out_layer = nn.Linear(num_output, self.hidden_dim)
self.norm3 = nn.LayerNorm(self.hidden_dim)
def forward(self, pro_features, roi_features):
'''
pro_features: (1, N * nr_boxes, self.d_model)
roi_features: (49, N * nr_boxes, self.d_model)
'''
features = roi_features.permute(1, 0, 2)
parameters = self.dynamic_layer(pro_features).permute(1, 0, 2)
param1 = parameters[:, :, :self.num_params].view(-1, self.hidden_dim, self.dim_dynamic)
param2 = parameters[:, :, self.num_params:].view(-1, self.dim_dynamic, self.hidden_dim)
features = torch.bmm(features, param1)
features = self.norm1(features)
features = self.activation(features)
features = torch.bmm(features, param2)
features = self.norm2(features)
features = self.activation(features)
features = features.flatten(1)
features = self.out_layer(features)
features = self.norm3(features)
features = self.activation(features)
return features
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}.")