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import torch
from mmengine.structures import InstanceData
from typing import List, Any
from mmpl.registry import MODELS
from mmseg.utils import SampleList
from .base_pler import BasePLer
import torch.nn.functional as F
from modules.sam import sam_model_registry
@MODELS.register_module()
class SegSAMPLer(BasePLer):
def __init__(self,
backbone,
sam_neck=None,
panoptic_head=None,
panoptic_fusion_head=None,
need_train_names=None,
train_cfg=None,
test_cfg=None,
*args, **kwargs):
super().__init__(*args, **kwargs)
self.save_hyperparameters()
self.need_train_names = need_train_names
backbone_type = backbone.pop('type')
self.backbone = sam_model_registry[backbone_type](**backbone)
if sam_neck is not None:
self.sam_neck = MODELS.build(sam_neck)
panoptic_head_ = panoptic_head.deepcopy()
panoptic_head_.update(train_cfg=train_cfg)
panoptic_head_.update(test_cfg=test_cfg)
self.panoptic_head = MODELS.build(panoptic_head_)
panoptic_fusion_head_ = panoptic_fusion_head.deepcopy()
panoptic_fusion_head_.update(test_cfg=test_cfg)
self.panoptic_fusion_head = MODELS.build(panoptic_fusion_head_)
self.num_things_classes = self.panoptic_head.num_things_classes
self.num_stuff_classes = self.panoptic_head.num_stuff_classes
self.num_classes = self.panoptic_head.num_classes
self.train_cfg = train_cfg
self.test_cfg = test_cfg
def setup(self, stage: str) -> None:
super().setup(stage)
if self.need_train_names is not None:
self._set_grad(self.need_train_names, noneed_train_names=[])
def init_weights(self):
import ipdb; ipdb.set_trace()
pass
def train(self, mode=True):
if self.need_train_names is not None:
return self._set_train_module(mode, self.need_train_names)
else:
super().train(mode)
return self
@torch.no_grad()
def extract_feat(self, batch_inputs):
feat, inter_features = self.backbone.image_encoder(batch_inputs)
return feat, inter_features
def validation_step(self, batch, batch_idx):
data = self.data_preprocessor(batch, False)
batch_inputs = data['inputs']
batch_data_samples = data['data_samples']
feats = self.extract_feat(batch_inputs)
if hasattr(self, 'sam_neck'):
feats = self.sam_neck(feats)
mask_cls_results, mask_pred_results = self.panoptic_head.predict(
feats, batch_data_samples)
else:
mask_cls_results, mask_pred_results = self.panoptic_head.predict(
feats, batch_data_samples, self.backbone)
results_list = self.panoptic_fusion_head.predict(
mask_cls_results,
mask_pred_results,
batch_data_samples,
rescale=True)
results = self.add_pred_to_datasample(batch_data_samples, results_list)
# preds = []
# targets = []
# for data_sample in results:
# result = dict()
# pred = data_sample.pred_instances
# result['boxes'] = pred['bboxes']
# result['scores'] = pred['scores']
# result['labels'] = pred['labels']
# if 'masks' in pred:
# result['masks'] = pred['masks']
# preds.append(result)
# # parse gt
# gt = dict()
# gt_data = data_sample.get('gt_instances', None)
# gt['boxes'] = gt_data['bboxes']
# gt['labels'] = gt_data['labels']
# if 'masks' in pred:
# gt['masks'] = gt_data['masks'].to_tensor(dtype=torch.bool, device=result['masks'].device)
# targets.append(gt)
#
# self.val_evaluator.update(preds, targets)
self.val_evaluator.update(batch, results)
def training_step(self, batch, batch_idx):
data = self.data_preprocessor(batch, True)
batch_inputs = data['inputs']
batch_data_samples = data['data_samples']
x = self.extract_feat(batch_inputs)
if hasattr(self, 'sam_neck'):
x = self.sam_neck(x)
losses = self.panoptic_head.loss(x, batch_data_samples)
else:
losses = self.panoptic_head.loss(x, batch_data_samples, self.backbone)
parsed_losses, log_vars = self.parse_losses(losses)
log_vars = {f'train_{k}': v for k, v in log_vars.items()}
log_vars['loss'] = parsed_losses
self.log_dict(log_vars, prog_bar=True)
return log_vars
def on_before_optimizer_step(self, optimizer) -> None:
self.log_grad(module=self.panoptic_head)
def add_pred_to_datasample(self, data_samples: SampleList,
results_list: List[dict]) -> SampleList:
"""Add predictions to `DetDataSample`.
Args:
data_samples (list[:obj:`DetDataSample`], optional): A batch of
data samples that contain annotations and predictions.
results_list (List[dict]): Instance segmentation, segmantic
segmentation and panoptic segmentation results.
Returns:
list[:obj:`DetDataSample`]: Detection results of the
input images. Each DetDataSample usually contain
'pred_instances' and `pred_panoptic_seg`. 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).
And the ``pred_panoptic_seg`` contains the following key
- sem_seg (Tensor): panoptic segmentation mask, has a
shape (1, h, w).
"""
for data_sample, pred_results in zip(data_samples, results_list):
if 'pan_results' in pred_results:
data_sample.pred_panoptic_seg = pred_results['pan_results']
if 'ins_results' in pred_results:
data_sample.pred_instances = pred_results['ins_results']
assert 'sem_results' not in pred_results, 'segmantic ' \
'segmentation results are not supported yet.'
return data_samples
def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any:
data = self.data_preprocessor(batch, False)
batch_inputs = data['inputs']
batch_data_samples = data['data_samples']
# import ipdb; ipdb.set_trace()
feats = self.extract_feat(batch_inputs)
if hasattr(self, 'sam_neck'):
feats = self.sam_neck(feats)
mask_cls_results, mask_pred_results = self.panoptic_head.predict(
feats, batch_data_samples)
else:
mask_cls_results, mask_pred_results = self.panoptic_head.predict(
feats, batch_data_samples, self.backbone)
results_list = self.panoptic_fusion_head.predict(
mask_cls_results,
mask_pred_results,
batch_data_samples,
rescale=True)
results = self.add_pred_to_datasample(batch_data_samples, results_list)
return results
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