| """Mask RCNN model implementation and runtime.""" |
|
|
| from __future__ import annotations |
|
|
| from typing import NamedTuple |
|
|
| import torch |
| from torch import nn |
|
|
| from vis4d.common.ckpt import load_model_checkpoint |
| from vis4d.op.base import BaseModel, ResNet |
| from vis4d.op.box.box2d import apply_mask, scale_and_clip_boxes |
| from vis4d.op.box.encoder import DeltaXYWHBBoxDecoder |
| from vis4d.op.detect.common import DetOut |
| from vis4d.op.detect.faster_rcnn import FasterRCNNHead, FRCNNOut |
| from vis4d.op.detect.mask_rcnn import ( |
| Det2Mask, |
| MaskOut, |
| MaskRCNNHead, |
| MaskRCNNHeadOut, |
| ) |
| from vis4d.op.detect.rcnn import RoI2Det |
| from vis4d.op.fpp.fpn import FPN |
|
|
|
|
| class MaskDetectionOut(NamedTuple): |
| """Mask detection output.""" |
|
|
| boxes: DetOut |
| masks: MaskOut |
|
|
|
|
| class MaskRCNNOut(NamedTuple): |
| """Mask RCNN output.""" |
|
|
| boxes: FRCNNOut |
| masks: MaskRCNNHeadOut |
|
|
|
|
| REV_KEYS = [ |
| (r"^backbone\.", "basemodel."), |
| (r"^rpn_head.rpn_reg\.", "rpn_head.rpn_box."), |
| (r"^roi_head.bbox_head\.", "roi_head."), |
| (r"^roi_head.mask_head\.", "mask_head."), |
| (r"^convs\.", "mask_head.convs."), |
| (r"^upsample\.", "mask_head.upsample."), |
| (r"^conv_logits\.", "mask_head.conv_logits."), |
| (r"^roi_head\.", "faster_rcnn_head.roi_head."), |
| (r"^rpn_head\.", "faster_rcnn_head.rpn_head."), |
| (r"^neck.lateral_convs\.", "fpn.inner_blocks."), |
| (r"^neck.fpn_convs\.", "fpn.layer_blocks."), |
| (r"\.conv.weight", ".weight"), |
| (r"\.conv.bias", ".bias"), |
| ] |
|
|
|
|
| class MaskRCNN(nn.Module): |
| """Mask RCNN model. |
| |
| Args: |
| num_classes (int): Number of classes. |
| basemodel (BaseModel, optional): Base model network. Defaults to |
| None. If None, will use ResNet50. |
| faster_rcnn_head (FasterRCNNHead, optional): Faster RCNN head. |
| Defaults to None. if None, will use default FasterRCNNHead. |
| mask_head (MaskRCNNHead, optional): Mask RCNN head. Defaults to |
| None. if None, will use default MaskRCNNHead. |
| rcnn_box_decoder (DeltaXYWHBBoxDecoder, optional): Decoder for RCNN |
| bounding boxes. Defaults to None. |
| no_overlap (bool, optional): Whether to remove overlapping pixels |
| between masks. Defaults to False. |
| weights (None | str, optional): Weights to load for model. If set |
| to "mmdet", will load MMDetection pre-trained weights. |
| Defaults to None. |
| """ |
|
|
| def __init__( |
| self, |
| num_classes: int, |
| basemodel: BaseModel | None = None, |
| faster_rcnn_head: FasterRCNNHead | None = None, |
| mask_head: MaskRCNNHead | None = None, |
| rcnn_box_decoder: DeltaXYWHBBoxDecoder | None = None, |
| no_overlap: bool = False, |
| weights: None | str = None, |
| ) -> None: |
| """Creates an instance of the class.""" |
| super().__init__() |
| self.basemodel = ( |
| ResNet(resnet_name="resnet50", pretrained=True, trainable_layers=3) |
| if basemodel is None |
| else basemodel |
| ) |
|
|
| self.fpn = FPN(self.basemodel.out_channels[2:], 256) |
|
|
| if faster_rcnn_head is None: |
| self.faster_rcnn_head = FasterRCNNHead(num_classes=num_classes) |
| else: |
| self.faster_rcnn_head = faster_rcnn_head |
|
|
| if mask_head is None: |
| self.mask_head = MaskRCNNHead(num_classes=num_classes) |
| else: |
| self.mask_head = mask_head |
|
|
| self.transform_outs = RoI2Det(rcnn_box_decoder) |
| self.det2mask = Det2Mask(no_overlap=no_overlap) |
|
|
| if weights is not None: |
| if weights == "mmdet": |
| weights = ( |
| "mmdet://mask_rcnn/mask_rcnn_r50_fpn_2x_coco/" |
| "mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392__segm_mAP-0.354_" |
| "20200505_003907-3e542a40.pth" |
| ) |
| if weights.startswith("mmdet://") or weights.startswith( |
| "bdd100k://" |
| ): |
| load_model_checkpoint(self, weights, rev_keys=REV_KEYS) |
| else: |
| load_model_checkpoint(self, weights) |
|
|
| def forward( |
| self, |
| images: torch.Tensor, |
| input_hw: list[tuple[int, int]], |
| boxes2d: None | list[torch.Tensor] = None, |
| boxes2d_classes: None | list[torch.Tensor] = None, |
| original_hw: None | list[tuple[int, int]] = None, |
| ) -> MaskRCNNOut | MaskDetectionOut: |
| """Forward pass. |
| |
| Args: |
| images (torch.Tensor): Input images. |
| input_hw (list[tuple[int, int]]): Input image resolutions. |
| boxes2d (None | list[torch.Tensor], optional): Bounding box |
| labels. Required for training. Defaults to None. |
| boxes2d_classes (None | list[torch.Tensor], optional): Class |
| labels. Required for training. Defaults to None. |
| original_hw (None | list[tuple[int, int]], optional): Original |
| image resolutions (before padding and resizing). Required for |
| testing. Defaults to None. |
| |
| Returns: |
| MaskRCNNOut | MaskDetectionOut: Either raw model |
| outputs (for training) or predicted outputs (for testing). |
| """ |
| if self.training: |
| assert boxes2d is not None and boxes2d_classes is not None |
| return self.forward_train( |
| images, input_hw, boxes2d, boxes2d_classes |
| ) |
| assert original_hw is not None |
| return self.forward_test(images, input_hw, original_hw) |
|
|
| def forward_train( |
| self, |
| images: torch.Tensor, |
| images_hw: list[tuple[int, int]], |
| target_boxes: list[torch.Tensor], |
| target_classes: list[torch.Tensor], |
| ) -> MaskRCNNOut: |
| """Forward training stage. |
| |
| Args: |
| images (torch.Tensor): Input images. |
| images_hw (list[tuple[int, int]]): Input image resolutions. |
| target_boxes (list[torch.Tensor]): Bounding box labels. Required |
| for training. Defaults to None. |
| target_classes (list[torch.Tensor]): Class labels. Required for |
| training. Defaults to None. |
| |
| Returns: |
| MaskRCNNOut: Raw model outputs. |
| """ |
| features = self.fpn(self.basemodel(images)) |
| outputs = self.faster_rcnn_head( |
| features, images_hw, target_boxes, target_classes |
| ) |
| assert outputs.sampled_proposals is not None |
| assert outputs.sampled_targets is not None |
| pos_proposals = apply_mask( |
| [torch.eq(label, 1) for label in outputs.sampled_targets.labels], |
| outputs.sampled_proposals.boxes, |
| )[0] |
| mask_outs = self.mask_head(features, pos_proposals) |
| return MaskRCNNOut(outputs, mask_outs) |
|
|
| def forward_test( |
| self, |
| images: torch.Tensor, |
| images_hw: list[tuple[int, int]], |
| original_hw: list[tuple[int, int]], |
| ) -> MaskDetectionOut: |
| """Forward testing stage. |
| |
| Args: |
| images (torch.Tensor): Input images. |
| images_hw (list[tuple[int, int]]): Input image resolutions. |
| original_hw (list[tuple[int, int]]): Original image resolutions |
| (before padding and resizing). |
| |
| Returns: |
| MaskDetectionOut: Predicted outputs. |
| """ |
| features = self.fpn(self.basemodel(images)) |
| outs = self.faster_rcnn_head(features, images_hw) |
| boxes, scores, class_ids = self.transform_outs( |
| *outs.roi, outs.proposals.boxes, images_hw |
| ) |
| mask_outs = self.mask_head(features, boxes) |
| for i, boxs in enumerate(boxes): |
| boxes[i] = scale_and_clip_boxes(boxs, original_hw[i], images_hw[i]) |
| mask_preds = [m.sigmoid() for m in mask_outs.mask_pred] |
| masks = self.det2mask( |
| mask_preds, boxes, scores, class_ids, original_hw |
| ) |
| return MaskDetectionOut(DetOut(boxes, scores, class_ids), masks) |
|
|
