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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 SegSAMDetPLer(BasePLer):
def __init__(self,
whole_model,
backbone,
neck=None,
panoptic_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
self.whole_model = MODELS.build(whole_model)
backbone_type = backbone.pop('type')
self.backbone = sam_model_registry[backbone_type](**backbone)
if neck is not None:
self.neck = MODELS.build(neck)
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
def validation_step(self, batch, batch_idx):
data = self.whole_model.data_preprocessor(batch, False)
batch_data_samples = self.whole_model._run_forward(data, mode='predict') # type: ignore
batch_inputs = data['inputs']
feat, inter_features = self.backbone.image_encoder(batch_inputs)
# import ipdb; ipdb.set_trace()
for idx, data_sample in enumerate(batch_data_samples):
bboxes = data_sample.pred_instances['bboxes']
ori_img_shape = data_sample.ori_shape
if len(bboxes) == 0:
im_mask = torch.zeros(
0,
ori_img_shape[0],
ori_img_shape[1],
device=self.device,
dtype=torch.bool)
else:
scale_factor = data_sample.scale_factor
repeat_num = int(bboxes.size(-1) / 2)
scale_factor = bboxes.new_tensor(scale_factor).repeat((1, repeat_num))
bboxes = bboxes * scale_factor
# Embed prompts
sparse_embeddings, dense_embeddings = self.backbone.prompt_encoder(
points=None,
boxes=bboxes,
masks=None,
)
# Predict masks
low_res_masks, iou_predictions = self.backbone.mask_decoder(
image_embeddings=feat[idx:idx + 1],
image_pe=self.backbone.prompt_encoder.get_dense_pe(),
sparse_prompt_embeddings=sparse_embeddings,
dense_prompt_embeddings=dense_embeddings,
multimask_output=False,
)
# Upscale the masks to the original image resolution
im_mask = F.interpolate(low_res_masks, ori_img_shape, mode="bilinear", align_corners=False)
im_mask = im_mask > 0
im_mask = im_mask.squeeze(1)
data_sample.pred_instances.masks = im_mask
self.val_evaluator.update(batch, batch_data_samples)
def training_step(self, batch, batch_idx):
data = self.whole_model.data_preprocessor(batch, True)
losses = self.whole_model._run_forward(data, mode='loss') # type: ignore
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.whole_model)
def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any:
data = self.whole_model.data_preprocessor(batch, False)
batch_data_samples = self.whole_model._run_forward(data, mode='predict') # type: ignore
batch_inputs = data['inputs']
feat, inter_features = self.backbone.image_encoder(batch_inputs)
# import ipdb; ipdb.set_trace()
for idx, data_sample in enumerate(batch_data_samples):
bboxes = data_sample.pred_instances['bboxes']
ori_img_shape = data_sample.ori_shape
if len(bboxes) == 0:
im_mask = torch.zeros(
0,
ori_img_shape[0],
ori_img_shape[1],
device=self.device,
dtype=torch.bool)
else:
scale_factor = data_sample.scale_factor
repeat_num = int(bboxes.size(-1) / 2)
scale_factor = bboxes.new_tensor(scale_factor).repeat((1, repeat_num))
bboxes = bboxes * scale_factor
# Embed prompts
sparse_embeddings, dense_embeddings = self.backbone.prompt_encoder(
points=None,
boxes=bboxes,
masks=None,
)
# Predict masks
low_res_masks, iou_predictions = self.backbone.mask_decoder(
image_embeddings=feat[idx:idx + 1],
image_pe=self.backbone.prompt_encoder.get_dense_pe(),
sparse_prompt_embeddings=sparse_embeddings,
dense_prompt_embeddings=dense_embeddings,
multimask_output=False,
)
# Upscale the masks to the original image resolution
im_mask = F.interpolate(low_res_masks, ori_img_shape, mode="bilinear", align_corners=False)
im_mask = im_mask > 0
im_mask = im_mask.squeeze(1)
data_sample.pred_instances.masks = im_mask
return batch_data_samples
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