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import copy
import warnings
from typing import List, Optional, Tuple, Union, Dict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmengine import ConfigDict
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.models import BaseDetector, TwoStageDetector, StandardRoIHead, SinePositionalEncoding, FCNMaskHead, \
BaseRoIHead
from mmdet.models.task_modules import SamplingResult
from mmdet.models.utils import multi_apply, unpack_gt_instances, empty_instances
from mmdet.structures import SampleList, DetDataSample
from mmdet.structures.bbox import bbox2roi
from mmdet.structures.mask import mask_target
from mmdet.utils import InstanceList, reduce_mean, OptMultiConfig
from mmpl.registry import MODELS, TASK_UTILS
from mmengine.model import BaseModel, BaseModule
from einops import rearrange, repeat
from mmpl.utils import ConfigType, OptConfigType
from mmdet.models.dense_heads import Mask2FormerHead
from mmdet.models.dense_heads.anchor_free_head import AnchorFreeHead
@MODELS.register_module()
class SAMInstanceHead(Mask2FormerHead):
def __init__(
self,
num_things_classes: int = 1,
num_stuff_classes: int = 0,
prompt_neck: ConfigType = ...,
with_iou: bool = False,
with_multiscale: bool = False,
with_sincos: bool = False,
with_res_imgfeat: bool = False,
loss_cls: ConfigType = dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=2.0,
reduction='mean',
class_weight=[1.0] * 133 + [0.1]),
loss_mask: ConfigType = dict(
type='CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=5.0),
loss_dice: ConfigType = dict(
type='DiceLoss',
use_sigmoid=True,
activate=True,
reduction='mean',
naive_dice=True,
eps=1.0,
loss_weight=5.0),
train_cfg: OptConfigType = None,
test_cfg: OptConfigType = None,
init_cfg: OptMultiConfig = None,
norm_cfg=dict(type='BN', requires_grad=True),
act_cfg=dict(type='ReLU', inplace=True),
**kwargs
):
super(AnchorFreeHead, self).__init__(init_cfg=init_cfg)
self.num_things_classes = num_things_classes
self.num_stuff_classes = num_stuff_classes
self.num_classes = self.num_things_classes + self.num_stuff_classes
self.with_iou = with_iou
self.with_multiscale = with_multiscale
self.with_sincos = with_sincos
self.with_res_imgfeat = with_res_imgfeat
# self.num_transformer_feat_level = num_transformer_feat_level
# self.num_heads = transformer_decoder.layer_cfg.cross_attn_cfg.num_heads
# self.num_transformer_decoder_layers = transformer_decoder.num_layers
# assert pixel_decoder.encoder.layer_cfg. \
# self_attn_cfg.num_levels == num_transformer_feat_level
# pixel_decoder_ = copy.deepcopy(pixel_decoder)
# pixel_decoder_.update(
# in_channels=in_channels,
# feat_channels=feat_channels,
# out_channels=out_channels)
# self.pixel_decoder = MODELS.build(pixel_decoder_)
# self.transformer_decoder = Mask2FormerTransformerDecoder(
# **transformer_decoder)
# self.decoder_embed_dims = self.transformer_decoder.embed_dims
#
# self.decoder_input_projs = ModuleList()
# # from low resolution to high resolution
# for _ in range(num_transformer_feat_level):
# if (self.decoder_embed_dims != feat_channels
# or enforce_decoder_input_project):
# self.decoder_input_projs.append(
# Conv2d(
# feat_channels, self.decoder_embed_dims, kernel_size=1))
# else:
# self.decoder_input_projs.append(nn.Identity())
# self.decoder_positional_encoding = SinePositionalEncoding(
# **positional_encoding)
# self.query_embed = nn.Embedding(self.num_queries, feat_channels)
# self.query_feat = nn.Embedding(self.num_queries, feat_channels)
# # from low resolution to high resolution
# self.level_embed = nn.Embedding(self.num_transformer_feat_level,
# feat_channels)
#
# self.cls_embed = nn.Linear(feat_channels, self.num_classes + 1)
# self.mask_embed = nn.Sequential(
# nn.Linear(feat_channels, feat_channels), nn.ReLU(inplace=True),
# nn.Linear(feat_channels, feat_channels), nn.ReLU(inplace=True),
# nn.Linear(feat_channels, out_channels))
self.prompt_neck = MODELS.build(prompt_neck)
self.num_queries = self.prompt_neck.num_queries
self.per_query_point = self.prompt_neck.per_query_point
out_channels = self.prompt_neck.out_channels
self.cls_embed = nn.Sequential(
nn.Linear(out_channels, out_channels // 2),
nn.ReLU(inplace=True),
nn.Linear(out_channels // 2, self.num_classes + 1)
)
if self.with_sincos:
self.point_emb = nn.Sequential(
nn.Linear(out_channels, out_channels),
nn.ReLU(inplace=True),
nn.Linear(out_channels, out_channels),
nn.ReLU(inplace=True),
nn.Linear(out_channels, self.per_query_point * out_channels*2)
)
else:
self.point_emb = nn.Sequential(
nn.Linear(out_channels, out_channels),
nn.ReLU(inplace=True),
nn.Linear(out_channels, out_channels),
nn.ReLU(inplace=True),
nn.Linear(out_channels, self.per_query_point * out_channels)
)
if self.with_res_imgfeat:
self.res_imgfeat = nn.Sequential(
nn.UpsamplingBilinear2d(scale_factor=2),
ConvModule(
out_channels,
out_channels,
kernel_size=3,
padding=1,
norm_cfg=norm_cfg,
act_cfg=act_cfg
)
)
self.test_cfg = test_cfg
self.train_cfg = train_cfg
if train_cfg:
self.assigner = TASK_UTILS.build(self.train_cfg['assigner'])
self.sampler = TASK_UTILS.build(
self.train_cfg['sampler'], default_args=dict(context=self))
self.num_points = self.train_cfg.get('num_points', 12544)
self.oversample_ratio = self.train_cfg.get('oversample_ratio', 3.0)
self.importance_sample_ratio = self.train_cfg.get(
'importance_sample_ratio', 0.75)
self.class_weight = loss_cls.class_weight
self.loss_cls = MODELS.build(loss_cls)
self.loss_mask = MODELS.build(loss_mask)
self.loss_dice = MODELS.build(loss_dice)
def forward(self, x: List[Tensor],
batch_data_samples: SampleList,
sam
) -> Tuple[List[Tensor]]:
"""Forward function.
Args:
x (list[Tensor]): Multi scale Features from the
upstream network, each is a 4D-tensor.
batch_data_samples (List[:obj:`DetDataSample`]): The Data
Samples. It usually includes information such as
`gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`.
Returns:
tuple[list[Tensor]]: A tuple contains two elements.
- cls_pred_list (list[Tensor)]: Classification logits \
for each decoder layer. Each is a 3D-tensor with shape \
(batch_size, num_queries, cls_out_channels). \
Note `cls_out_channels` should includes background.
- mask_pred_list (list[Tensor]): Mask logits for each \
decoder layer. Each with shape (batch_size, num_queries, \
h, w).
"""
batch_img_metas = [
data_sample.metainfo for data_sample in batch_data_samples
]
batch_size = len(batch_img_metas)
decoder_out, query_feat_list, res_img_feat = self.prompt_neck(x)
if self.with_multiscale:
cls_pred_list = [self.cls_embed(query_feat) for query_feat in query_feat_list]
else:
# shape (batch_size， num_queries, c)
cls_pred_list = [self.cls_embed(decoder_out)]
# shape (batch_size, num_queries, c)
point_emb = self.point_emb(decoder_out)
# shape (batch_size, num_queries, per_query_point, c)
point_emb = point_emb.view(batch_size, self.num_queries, self.per_query_point, -1)
img_seg_feat = x[0]
point_emb = rearrange(point_emb, 'b n p c -> (b n) p c')
if self.with_sincos:
point_emb = torch.sin(point_emb[..., ::2]) + point_emb[..., 1::2]
nomask_dense_embeddings = sam.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
point_emb.shape[0], -1, *img_seg_feat.shape[-2:]
)
img_embeddings = torch.repeat_interleave(img_seg_feat, self.num_queries, dim=0)
img_pe = sam.prompt_encoder.get_dense_pe()
img_pe = repeat(img_pe, 'b c h w -> (b n) c h w', n=img_embeddings.shape[0])
if self.with_res_imgfeat:
res_img_feat = self.res_imgfeat(res_img_feat)
res_img_feat = torch.repeat_interleave(res_img_feat, self.num_queries, dim=0)
else:
res_img_feat = None
low_res_masks, iou_predictions = sam.mask_decoder.forward_batch(
image_embeddings=img_embeddings,
image_pe=img_pe,
sparse_prompt_embeddings=point_emb,
dense_prompt_embeddings=nomask_dense_embeddings,
multimask_output=False,
res_img_feat=res_img_feat,
)
mask_pred = rearrange(low_res_masks.squeeze(1), '(b n) h w -> b n h w', b=batch_size)
# optional
# if self.with_iou:
# iou_predictions = iou_predictions.view(batch_size, self.num_queries, -1)
# cls_pred = cls_pred * iou_predictions
if self.with_multiscale:
mask_pred_list = [mask_pred] * len(cls_pred_list)
else:
mask_pred_list = [mask_pred]
return cls_pred_list, mask_pred_list
def predict(self, x: Tuple[Tensor],
batch_data_samples: SampleList,
sam
) -> Tuple[Tensor]:
"""Test without augmentaton.
Args:
x (tuple[Tensor]): Multi-level features from the
upstream network, each is a 4D-tensor.
batch_data_samples (List[:obj:`DetDataSample`]): The Data
Samples. It usually includes information such as
`gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`.
Returns:
tuple[Tensor]: A tuple contains two tensors.
- mask_cls_results (Tensor): Mask classification logits,\
shape (batch_size, num_queries, cls_out_channels).
Note `cls_out_channels` should includes background.
- mask_pred_results (Tensor): Mask logits, shape \
(batch_size, num_queries, h, w).
"""
batch_img_metas = [
data_sample.metainfo for data_sample in batch_data_samples
]
all_cls_scores, all_mask_preds = self(x, batch_data_samples, sam)
mask_cls_results = all_cls_scores[-1]
mask_pred_results = all_mask_preds[-1]
# upsample masks
img_shape = batch_img_metas[0]['batch_input_shape']
mask_pred_results = F.interpolate(
mask_pred_results,
size=(img_shape[0], img_shape[1]),
mode='bilinear',
align_corners=False)
return mask_cls_results, mask_pred_results
def loss(
self,
x: Tuple[Tensor],
batch_data_samples: SampleList,
sam,
) -> Dict[str, Tensor]:
"""Perform forward propagation and loss calculation of the panoptic
head on the features of the upstream network.
Args:
x (tuple[Tensor]): Multi-level features from the upstream
network, each is a 4D-tensor.
batch_data_samples (List[:obj:`DetDataSample`]): The Data
Samples. It usually includes information such as
`gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
batch_img_metas = []
batch_gt_instances = []
batch_gt_semantic_segs = []
for data_sample in batch_data_samples:
batch_img_metas.append(data_sample.metainfo)
batch_gt_instances.append(data_sample.gt_instances)
if 'gt_sem_seg' in data_sample:
batch_gt_semantic_segs.append(data_sample.gt_sem_seg)
else:
batch_gt_semantic_segs.append(None)
# forward
all_cls_scores, all_mask_preds = self(x, batch_data_samples, sam)
# preprocess ground truth
batch_gt_instances = self.preprocess_gt(batch_gt_instances,
batch_gt_semantic_segs)
# loss
losses = self.loss_by_feat(all_cls_scores, all_mask_preds,
batch_gt_instances, batch_img_metas)
return losses
@MODELS.register_module()
class SAMAnchorInstanceHead(TwoStageDetector):
def __init__(
self,
sam_head=True,
neck: OptConfigType = None,
rpn_head: OptConfigType = None,
roi_head: OptConfigType = None,
train_cfg: OptConfigType = None,
test_cfg: OptConfigType = None,
**kwargs
):
super(TwoStageDetector, self).__init__()
self.neck = MODELS.build(neck)
self.sam_head = sam_head
if rpn_head is not None:
rpn_train_cfg = train_cfg.rpn if train_cfg is not None else None
rpn_head_ = rpn_head.copy()
rpn_head_.update(train_cfg=rpn_train_cfg, test_cfg=test_cfg.rpn)
rpn_head_num_classes = rpn_head_.get('num_classes', None)
if rpn_head_num_classes is None:
rpn_head_.update(num_classes=1)
else:
if rpn_head_num_classes != 1:
warnings.warn(
'The `num_classes` should be 1 in RPN, but get '
f'{rpn_head_num_classes}, please set '
'rpn_head.num_classes = 1 in your config file.')
rpn_head_.update(num_classes=1)
self.rpn_head = MODELS.build(rpn_head_)
if roi_head is not None:
# update train and test cfg here for now
# TODO: refactor assigner & sampler
rcnn_train_cfg = train_cfg.rcnn if train_cfg is not None else None
roi_head.update(train_cfg=rcnn_train_cfg)
roi_head.update(test_cfg=test_cfg.rcnn)
self.roi_head = MODELS.build(roi_head)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
def extract_feat(self, x):
x = self.neck(x)
return x
def loss(self,
batch_inputs,
batch_data_samples: SampleList,
sam
) -> dict:
"""Calculate losses from a batch of inputs and data samples.
Args:
batch_inputs (Tensor): Input images of shape (N, C, H, W).
These should usually be mean centered and std scaled.
batch_data_samples (List[:obj:`DetDataSample`]): The batch
data samples. It usually includes information such
as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
Returns:
dict: A dictionary of loss components
"""
x = self.extract_feat(batch_inputs)
img_seg_feat = batch_inputs[0]
losses = dict()
# RPN forward and loss
if self.with_rpn:
proposal_cfg = self.train_cfg.get('rpn_proposal',
self.test_cfg.rpn)
rpn_data_samples = copy.deepcopy(batch_data_samples)
# set cat_id of gt_labels to 0 in RPN
for data_sample in rpn_data_samples:
data_sample.gt_instances.labels = \
torch.zeros_like(data_sample.gt_instances.labels)
rpn_losses, rpn_results_list = self.rpn_head.loss_and_predict(
x, rpn_data_samples, proposal_cfg=proposal_cfg)
# avoid get same name with roi_head loss
keys = rpn_losses.keys()
for key in list(keys):
if 'loss' in key and 'rpn' not in key:
rpn_losses[f'rpn_{key}'] = rpn_losses.pop(key)
losses.update(rpn_losses)
else:
assert batch_data_samples[0].get('proposals', None) is not None
# use pre-defined proposals in InstanceData for the second stage
# to extract ROI features.
rpn_results_list = [
data_sample.proposals for data_sample in batch_data_samples
]
if self.sam_head:
roi_losses = self.roi_head.loss(x, rpn_results_list,
batch_data_samples,
sam, img_seg_feat
)
else:
roi_losses = self.roi_head.loss(x, rpn_results_list,
batch_data_samples
)
losses.update(roi_losses)
return losses
def predict(self,
batch_inputs: Tensor,
batch_data_samples: SampleList,
sam,
rescale: bool = True
) -> SampleList:
"""Predict results from a batch of inputs and data samples with post-
processing.
Args:
batch_inputs (Tensor): Inputs with shape (N, C, H, W).
batch_data_samples (List[:obj:`DetDataSample`]): The Data
Samples. It usually includes information such as
`gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`.
rescale (bool): Whether to rescale the results.
Defaults to True.
Returns:
list[:obj:`DetDataSample`]: Return the detection results of the
input images. The returns value is DetDataSample,
which usually contain 'pred_instances'. And the
``pred_instances`` usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
- masks (Tensor): Has a shape (num_instances, H, W).
"""
assert self.with_bbox, 'Bbox head must be implemented.'
x = self.extract_feat(batch_inputs)
img_seg_feat = batch_inputs[0]
# If there are no pre-defined proposals, use RPN to get proposals
if batch_data_samples[0].get('proposals', None) is None:
rpn_results_list = self.rpn_head.predict(
x, batch_data_samples, rescale=False)
else:
rpn_results_list = [
data_sample.proposals for data_sample in batch_data_samples
]
if self.sam_head:
results_list = self.roi_head.predict(
x, rpn_results_list, batch_data_samples, sam, img_seg_feat, rescale=rescale)
else:
results_list = self.roi_head.predict(
x, rpn_results_list, batch_data_samples, rescale=rescale)
batch_data_samples = self.add_pred_to_datasample(
batch_data_samples, results_list)
return batch_data_samples
@MODELS.register_module()
class SAMAnchorPromptRoIHead(StandardRoIHead):
def __init__(
self,
positional_encoding=dict(num_feats=128, normalize=True),
*args,
**kwargs
):
super(StandardRoIHead, self).__init__(*args, **kwargs)
self.generator_pe = SinePositionalEncoding(**positional_encoding)
def _mask_forward(self,
x: Tuple[Tensor],
rois: Tensor = None,
pos_inds: Optional[Tensor] = None,
bbox_feats: Optional[Tensor] = None,
sam=None, img_seg_feat=None
) -> dict:
"""Mask head forward function used in both training and testing.
Args:
x (tuple[Tensor]): Tuple of multi-level img features.
rois (Tensor): RoIs with the shape (n, 5) where the first
column indicates batch id of each RoI.
pos_inds (Tensor, optional): Indices of positive samples.
Defaults to None.
bbox_feats (Tensor): Extract bbox RoI features. Defaults to None.
Returns:
dict[str, Tensor]: Usually returns a dictionary with keys:
- `mask_preds` (Tensor): Mask prediction.
- `mask_feats` (Tensor): Extract mask RoI features.
"""
assert ((rois is not None) ^
(pos_inds is not None and bbox_feats is not None))
if rois is not None:
mask_feats = self.mask_roi_extractor(
x[:self.mask_roi_extractor.num_inputs], rois)
if self.with_shared_head:
mask_feats = self.shared_head(mask_feats)
else:
assert bbox_feats is not None
mask_feats = bbox_feats[pos_inds]
mask_preds = self.mask_head(mask_feats, sam, img_seg_feat, img_flag_ids=rois[:, 0])
mask_results = dict(mask_preds=mask_preds[0], mask_iou=mask_preds[1], mask_feats=mask_feats)
return mask_results
def mask_loss(self, x: Tuple[Tensor],
sampling_results: List[SamplingResult], bbox_feats: Tensor,
batch_gt_instances: InstanceList,
sam, img_seg_feat
) -> dict:
"""Perform forward propagation and loss calculation of the mask head on
the features of the upstream network.
Args:
x (tuple[Tensor]): Tuple of multi-level img features.
sampling_results (list["obj:`SamplingResult`]): Sampling results.
bbox_feats (Tensor): Extract bbox RoI features.
batch_gt_instances (list[:obj:`InstanceData`]): Batch of
gt_instance. It usually includes ``bboxes``, ``labels``, and
``masks`` attributes.
Returns:
dict: Usually returns a dictionary with keys:
- `mask_preds` (Tensor): Mask prediction.
- `mask_feats` (Tensor): Extract mask RoI features.
- `mask_targets` (Tensor): Mask target of each positive\
proposals in the image.
- `loss_mask` (dict): A dictionary of mask loss components.
"""
if not self.share_roi_extractor:
pos_rois = bbox2roi([res.pos_priors for res in sampling_results])
mask_results = self._mask_forward(
x, pos_rois, sam=sam, img_seg_feat=img_seg_feat)
else:
pos_inds = []
device = bbox_feats.device
for res in sampling_results:
pos_inds.append(
torch.ones(
res.pos_priors.shape[0],
device=device,
dtype=torch.uint8))
pos_inds.append(
torch.zeros(
res.neg_priors.shape[0],
device=device,
dtype=torch.uint8))
pos_inds = torch.cat(pos_inds)
mask_results = self._mask_forward(
x, pos_inds=pos_inds, bbox_feats=bbox_feats)
mask_loss_and_target = self.mask_head.loss_and_target(
mask_preds=mask_results['mask_preds'],
sampling_results=sampling_results,
batch_gt_instances=batch_gt_instances,
rcnn_train_cfg=self.train_cfg)
mask_results.update(loss_mask=mask_loss_and_target['loss_mask'])
return mask_results
def loss(self, x: Tuple[Tensor], rpn_results_list: InstanceList,
batch_data_samples: List[DetDataSample],
sam, img_seg_feat
) -> dict:
"""Perform forward propagation and loss calculation of the detection
roi on the features of the upstream network.
Args:
x (tuple[Tensor]): List of multi-level img features.
rpn_results_list (list[:obj:`InstanceData`]): List of region
proposals.
batch_data_samples (list[:obj:`DetDataSample`]): The batch
data samples. It usually includes information such
as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
Returns:
dict[str, Tensor]: A dictionary of loss components
"""
x = list(x)
bs, _, h, w = x[-1].shape
mask_pe = torch.zeros((bs, h, w), device=x[0].device, dtype=torch.bool)
img_feats_pe = self.generator_pe(mask_pe)
for i in range(len(x)):
x[i] = x[i] + torch.nn.functional.interpolate(img_feats_pe, size=x[i].shape[-2:], mode='bilinear')
assert len(rpn_results_list) == len(batch_data_samples)
outputs = unpack_gt_instances(batch_data_samples)
batch_gt_instances, batch_gt_instances_ignore, _ = outputs
# assign gts and sample proposals
num_imgs = len(batch_data_samples)
sampling_results = []
for i in range(num_imgs):
# rename rpn_results.bboxes to rpn_results.priors
rpn_results = rpn_results_list[i]
rpn_results.priors = rpn_results.pop('bboxes')
assign_result = self.bbox_assigner.assign(
rpn_results, batch_gt_instances[i],
batch_gt_instances_ignore[i])
sampling_result = self.bbox_sampler.sample(
assign_result,
rpn_results,
batch_gt_instances[i],
feats=[lvl_feat[i][None] for lvl_feat in x])
sampling_results.append(sampling_result)
losses = dict()
# bbox head loss
if self.with_bbox:
bbox_results = self.bbox_loss(x, sampling_results)
losses.update(bbox_results['loss_bbox'])
# mask head forward and loss
if self.with_mask:
mask_results = self.mask_loss(x, sampling_results,
bbox_results['bbox_feats'],
batch_gt_instances,
sam, img_seg_feat
)
losses.update(mask_results['loss_mask'])
return losses
def predict_mask(self,
x: Tuple[Tensor],
batch_img_metas: List[dict],
results_list: InstanceList,
rescale: bool = False,
sam=None, img_seg_feat=None
) -> InstanceList:
"""Perform forward propagation of the mask head and predict detection
results on the features of the upstream network.
Args:
x (tuple[Tensor]): Feature maps of all scale level.
batch_img_metas (list[dict]): List of image information.
results_list (list[:obj:`InstanceData`]): Detection results of
each image.
rescale (bool): If True, return boxes in original image space.
Defaults to False.
Returns:
list[:obj:`InstanceData`]: Detection results of each image
after the post process.
Each item usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
- masks (Tensor): Has a shape (num_instances, H, W).
"""
# don't need to consider aug_test.
bboxes = [res.bboxes for res in results_list]
mask_rois = bbox2roi(bboxes)
if mask_rois.shape[0] == 0:
results_list = empty_instances(
batch_img_metas,
mask_rois.device,
task_type='mask',
instance_results=results_list,
mask_thr_binary=self.test_cfg.mask_thr_binary)
return results_list
mask_results = self._mask_forward(x, mask_rois, sam=sam, img_seg_feat=img_seg_feat)
mask_preds = mask_results['mask_preds']
# split batch mask prediction back to each image
num_mask_rois_per_img = [len(res) for res in results_list]
mask_preds = mask_preds.split(num_mask_rois_per_img, 0)
# TODO: Handle the case where rescale is false
results_list = self.mask_head.predict_by_feat(
mask_preds=mask_preds,
results_list=results_list,
batch_img_metas=batch_img_metas,
rcnn_test_cfg=self.test_cfg,
rescale=rescale)
return results_list
def predict(self,
x: Tuple[Tensor],
rpn_results_list: InstanceList,
batch_data_samples: SampleList,
sam, img_seg_feat,
rescale: bool = False) -> InstanceList:
"""Perform forward propagation of the roi head and predict detection
results on the features of the upstream network.
Args:
x (tuple[Tensor]): Features from upstream network. Each
has shape (N, C, H, W).
rpn_results_list (list[:obj:`InstanceData`]): list of region
proposals.
batch_data_samples (List[:obj:`DetDataSample`]): The Data
Samples. It usually includes information such as
`gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`.
rescale (bool): Whether to rescale the results to
the original image. Defaults to True.
Returns:
list[obj:`InstanceData`]: Detection results of each image.
Each item usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
- masks (Tensor): Has a shape (num_instances, H, W).
"""
x = list(x)
bs, _, h, w = x[-1].shape
mask_pe = torch.zeros((bs, h, w), device=x[0].device, dtype=torch.bool)
img_feats_pe = self.generator_pe(mask_pe)
for i in range(len(x)):
x[i] = x[i] + torch.nn.functional.interpolate(img_feats_pe, size=x[i].shape[-2:], mode='bilinear')
assert self.with_bbox, 'Bbox head must be implemented.'
batch_img_metas = [
data_samples.metainfo for data_samples in batch_data_samples
]
# TODO: nms_op in mmcv need be enhanced, the bbox result may get
# difference when not rescale in bbox_head
# If it has the mask branch, the bbox branch does not need
# to be scaled to the original image scale, because the mask
# branch will scale both bbox and mask at the same time.
bbox_rescale = rescale if not self.with_mask else False
results_list = self.predict_bbox(
x,
batch_img_metas,
rpn_results_list,
rcnn_test_cfg=self.test_cfg,
rescale=bbox_rescale)
if self.with_mask:
results_list = self.predict_mask(
x, batch_img_metas, results_list, rescale=rescale, sam=sam, img_seg_feat=img_seg_feat)
return results_list
@MODELS.register_module()
class SAMPromptMaskHead(FCNMaskHead):
def __init__(self,
per_query_point: int = 5,
with_sincos: bool = True,
class_agnostic: bool = False,
loss_mask: ConfigType = dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0),
*args,
**kwargs
) -> None:
super(BaseModule, self).__init__()
self.per_query_point = per_query_point
self.with_sincos = with_sincos
self.class_agnostic = class_agnostic
self.loss_mask = MODELS.build(loss_mask)
if with_sincos:
sincos = 2
else:
sincos = 1
self.point_emb = nn.Sequential(
nn.Conv2d(256, 256, 3, stride=2, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Flatten(),
nn.Linear(7*7*256, 256),
nn.ReLU(inplace=True),
nn.Linear(256, 256),
nn.ReLU(inplace=True),
nn.Linear(256, 256*sincos*per_query_point)
)
def forward(self, x, sam, img_seg_feat, img_flag_ids) -> Tensor:
batch_size = x.shape[0]
point_emb = self.point_emb(x)
point_emb = point_emb.view(batch_size, self.per_query_point, -1)
if self.with_sincos:
point_emb = torch.sin(point_emb[..., ::2]) + point_emb[..., 1::2]
nomask_dense_embeddings = sam.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
point_emb.shape[0], -1, *img_seg_feat.shape[-2:]
)
img_flag_ids = torch.bincount(img_flag_ids.long())
padding = torch.zeros((len(img_seg_feat)-len(img_flag_ids),), device=img_flag_ids.device, dtype=img_flag_ids.dtype)
img_flag_ids = torch.cat([img_flag_ids, padding])
img_embeddings = torch.repeat_interleave(img_seg_feat, img_flag_ids, dim=0)
img_pe = sam.prompt_encoder.get_dense_pe()
img_pe = repeat(img_pe, 'b c h w -> (b n) c h w', n=img_embeddings.shape[0])
res_img_feat = None
low_res_masks, iou_predictions = sam.mask_decoder.forward_batch(
image_embeddings=img_embeddings,
image_pe=img_pe,
sparse_prompt_embeddings=point_emb,
dense_prompt_embeddings=nomask_dense_embeddings,
multimask_output=False,
res_img_feat=res_img_feat,
)
mask_pred = low_res_masks.squeeze(1)
iou_predictions = iou_predictions.squeeze(1)
return mask_pred, iou_predictions
def get_targets(self, sampling_results: List[SamplingResult],
batch_gt_instances: InstanceList,
rcnn_train_cfg: ConfigDict) -> Tensor:
"""Calculate the ground truth for all samples in a batch according to
the sampling_results.
Args:
sampling_results (List[obj:SamplingResult]): Assign results of
all images in a batch after sampling.
batch_gt_instances (list[:obj:`InstanceData`]): Batch of
gt_instance. It usually includes ``bboxes``, ``labels``, and
``masks`` attributes.
rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN.
Returns:
Tensor: Mask target of each positive proposals in the image.
"""
pos_proposals = [res.pos_priors for res in sampling_results]
pos_assigned_gt_inds = [
res.pos_assigned_gt_inds for res in sampling_results
]
gt_masks = [res.masks for res in batch_gt_instances]
mask_targets_list = []
mask_size = (rcnn_train_cfg.mask_size,) * 2
device = pos_proposals[0].device
for pos_gt_inds, gt_mask in zip(pos_assigned_gt_inds, gt_masks):
if len(pos_gt_inds) == 0:
mask_targets = torch.zeros((0,) + mask_size, device=device, dytpe=torch.float32)
else:
mask_targets = gt_mask[pos_gt_inds.cpu()].to_tensor(dtype=torch.float32, device=device)
mask_targets_list.append(mask_targets)
mask_targets = torch.cat(mask_targets_list)
return mask_targets
def loss_and_target(self, mask_preds: Tensor,
sampling_results: List[SamplingResult],
batch_gt_instances: InstanceList,
rcnn_train_cfg: ConfigDict) -> dict:
"""Calculate the loss based on the features extracted by the mask head.
Args:
mask_preds (Tensor): Predicted foreground masks, has shape
(num_pos, num_classes, h, w).
sampling_results (List[obj:SamplingResult]): Assign results of
all images in a batch after sampling.
batch_gt_instances (list[:obj:`InstanceData`]): Batch of
gt_instance. It usually includes ``bboxes``, ``labels``, and
``masks`` attributes.
rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN.
Returns:
dict: A dictionary of loss and targets components.
"""
mask_targets = self.get_targets(
sampling_results=sampling_results,
batch_gt_instances=batch_gt_instances,
rcnn_train_cfg=rcnn_train_cfg)
pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
mask_preds = torch.nn.functional.interpolate(
mask_preds.unsqueeze(1), size=mask_targets.shape[-2:], mode='bilinear', align_corners=False)
loss = dict()
if mask_preds.size(0) == 0:
loss_mask = mask_preds.sum()
else:
if self.class_agnostic:
loss_mask = self.loss_mask(mask_preds, mask_targets,
torch.zeros_like(pos_labels))
else:
loss_mask = self.loss_mask(mask_preds, mask_targets,
pos_labels)
loss['loss_mask'] = loss_mask
# TODO: which algorithm requires mask_targets?
return dict(loss_mask=loss, mask_targets=mask_targets)
def _predict_by_feat_single(self,
mask_preds: Tensor,
bboxes: Tensor,
labels: Tensor,
img_meta: dict,
rcnn_test_cfg: ConfigDict,
rescale: bool = False,
activate_map: bool = False) -> Tensor:
"""Get segmentation masks from mask_preds and bboxes.
Args:
mask_preds (Tensor): Predicted foreground masks, has shape
(n, num_classes, h, w).
bboxes (Tensor): Predicted bboxes, has shape (n, 4)
labels (Tensor): Labels of bboxes, has shape (n, )
img_meta (dict): image information.
rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head.
Defaults to None.
rescale (bool): If True, return boxes in original image space.
Defaults to False.
activate_map (book): Whether get results with augmentations test.
If True, the `mask_preds` will not process with sigmoid.
Defaults to False.
Returns:
Tensor: Encoded masks, has shape (n, img_w, img_h)
Example:
>>> from mmengine.config import Config
>>> from mmdet.models.roi_heads.mask_heads.fcn_mask_head import * # NOQA
>>> N = 7 # N = number of extracted ROIs
>>> C, H, W = 11, 32, 32
>>> # Create example instance of FCN Mask Head.
>>> self = FCNMaskHead(num_classes=C, num_convs=0)
>>> inputs = torch.rand(N, self.in_channels, H, W)
>>> mask_preds = self.forward(inputs)
>>> # Each input is associated with some bounding box
>>> bboxes = torch.Tensor([[1, 1, 42, 42 ]] * N)
>>> labels = torch.randint(0, C, size=(N,))
>>> rcnn_test_cfg = Config({'mask_thr_binary': 0, })
>>> ori_shape = (H * 4, W * 4)
>>> scale_factor = (1, 1)
>>> rescale = False
>>> img_meta = {'scale_factor': scale_factor,
... 'ori_shape': ori_shape}
>>> # Encoded masks are a list for each category.
>>> encoded_masks = self._get_seg_masks_single(
... mask_preds, bboxes, labels,
... img_meta, rcnn_test_cfg, rescale)
>>> assert encoded_masks.size()[0] == N
>>> assert encoded_masks.size()[1:] == ori_shape
"""
scale_factor = bboxes.new_tensor(img_meta['scale_factor']).repeat(
(1, 2))
img_h, img_w = img_meta['ori_shape'][:2]
device = bboxes.device
if not activate_map:
mask_preds = mask_preds.sigmoid()
else:
# In AugTest, has been activated before
mask_preds = bboxes.new_tensor(mask_preds)
if rescale: # in-placed rescale the bboxes
bboxes /= scale_factor
else:
w_scale, h_scale = scale_factor[0, 0], scale_factor[0, 1]
img_h = np.round(img_h * h_scale.item()).astype(np.int32)
img_w = np.round(img_w * w_scale.item()).astype(np.int32)
threshold = rcnn_test_cfg.mask_thr_binary
im_mask = torch.nn.functional.interpolate(
mask_preds.unsqueeze(1), size=(img_h, img_w), mode='bilinear', align_corners=False).squeeze(1)
if threshold >= 0:
im_mask = im_mask >= threshold
else:
# for visualization and debugging
im_mask = (im_mask * 255).to(dtype=torch.uint8)
return im_mask |