Source code for mmdet.models.detectors.yolact

import torch

from mmdet.core import bbox2result
from ..builder import DETECTORS, build_head
from .single_stage import SingleStageDetector

[docs]@DETECTORS.register_module() class YOLACT(SingleStageDetector): """Implementation of `YOLACT <>`_""" def __init__(self, backbone, neck, bbox_head, segm_head, mask_head, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None): super(YOLACT, self).__init__(backbone, neck, bbox_head, train_cfg, test_cfg, pretrained, init_cfg) self.segm_head = build_head(segm_head) self.mask_head = build_head(mask_head)
[docs] def forward_dummy(self, img): """Used for computing network flops. See `mmdetection/tools/analysis_tools/` """ raise NotImplementedError
[docs] def forward_train(self, img, img_metas, gt_bboxes, gt_labels, gt_bboxes_ignore=None, gt_masks=None): """ Args: img (Tensor): of shape (N, C, H, W) encoding input images. Typically these should be mean centered and std scaled. img_metas (list[dict]): list of image info dict where each dict has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on the values of these keys see `mmdet/datasets/pipelines/`. gt_bboxes (list[Tensor]): Ground truth bboxes for each image with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. gt_labels (list[Tensor]): class indices corresponding to each box gt_bboxes_ignore (None | list[Tensor]): specify which bounding boxes can be ignored when computing the loss. gt_masks (None | Tensor) : true segmentation masks for each box used if the architecture supports a segmentation task. Returns: dict[str, Tensor]: a dictionary of loss components """ # convert Bitmap mask or Polygon Mask to Tensor here gt_masks = [ gt_mask.to_tensor(dtype=torch.uint8, device=img.device) for gt_mask in gt_masks ] x = self.extract_feat(img) cls_score, bbox_pred, coeff_pred = self.bbox_head(x) bbox_head_loss_inputs = (cls_score, bbox_pred) + (gt_bboxes, gt_labels, img_metas) losses, sampling_results = self.bbox_head.loss( *bbox_head_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) segm_head_outs = self.segm_head(x[0]) loss_segm = self.segm_head.loss(segm_head_outs, gt_masks, gt_labels) losses.update(loss_segm) mask_pred = self.mask_head(x[0], coeff_pred, gt_bboxes, img_metas, sampling_results) loss_mask = self.mask_head.loss(mask_pred, gt_masks, gt_bboxes, img_metas, sampling_results) losses.update(loss_mask) # check NaN and Inf for loss_name in losses.keys(): assert torch.isfinite(torch.stack(losses[loss_name]))\ .all().item(), '{} becomes infinite or NaN!'\ .format(loss_name) return losses
[docs] def simple_test(self, img, img_metas, rescale=False): """Test function without test-time augmentation.""" feat = self.extract_feat(img) det_bboxes, det_labels, det_coeffs = self.bbox_head.simple_test( feat, img_metas, rescale=rescale) bbox_results = [ bbox2result(det_bbox, det_label, self.bbox_head.num_classes) for det_bbox, det_label in zip(det_bboxes, det_labels) ] segm_results = self.mask_head.simple_test( feat, det_bboxes, det_labels, det_coeffs, img_metas, rescale=rescale) return list(zip(bbox_results, segm_results))
[docs] def aug_test(self, imgs, img_metas, rescale=False): """Test with augmentations.""" raise NotImplementedError( 'YOLACT does not support test-time augmentation')