160 lines
6.0 KiB
Python
160 lines
6.0 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import warnings
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import mmcv
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import torch
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from mmcv.image import tensor2imgs
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from mmdet.core import bbox_mapping
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from ..builder import DETECTORS, build_backbone, build_head, build_neck
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from .base import BaseDetector
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@DETECTORS.register_module()
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class RPN(BaseDetector):
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"""Implementation of Region Proposal Network."""
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def __init__(self,
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backbone,
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neck,
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rpn_head,
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train_cfg,
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test_cfg,
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pretrained=None,
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init_cfg=None):
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super(RPN, self).__init__(init_cfg)
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if pretrained:
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warnings.warn('DeprecationWarning: pretrained is deprecated, '
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'please use "init_cfg" instead')
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backbone.pretrained = pretrained
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self.backbone = build_backbone(backbone)
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self.neck = build_neck(neck) if neck is not None else None
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rpn_train_cfg = train_cfg.rpn if train_cfg is not None else None
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rpn_head.update(train_cfg=rpn_train_cfg)
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rpn_head.update(test_cfg=test_cfg.rpn)
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self.rpn_head = build_head(rpn_head)
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self.train_cfg = train_cfg
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self.test_cfg = test_cfg
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def extract_feat(self, img):
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"""Extract features.
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Args:
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img (torch.Tensor): Image tensor with shape (n, c, h ,w).
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Returns:
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list[torch.Tensor]: Multi-level features that may have
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different resolutions.
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"""
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x = self.backbone(img)
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if self.with_neck:
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x = self.neck(x)
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return x
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def forward_dummy(self, img):
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"""Dummy forward function."""
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x = self.extract_feat(img)
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rpn_outs = self.rpn_head(x)
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return rpn_outs
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def forward_train(self,
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img,
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img_metas,
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gt_bboxes=None,
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gt_bboxes_ignore=None):
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"""
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Args:
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img (Tensor): Input images of shape (N, C, H, W).
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Typically these should be mean centered and std scaled.
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img_metas (list[dict]): A List of image info dict where each dict
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has: 'img_shape', 'scale_factor', 'flip', and may also contain
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'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
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For details on the values of these keys see
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:class:`mmdet.datasets.pipelines.Collect`.
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gt_bboxes (list[Tensor]): Each item are the truth boxes for each
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image in [tl_x, tl_y, br_x, br_y] format.
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gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
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boxes can be ignored when computing the loss.
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Returns:
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dict[str, Tensor]: A dictionary of loss components.
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"""
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if (isinstance(self.train_cfg.rpn, dict)
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and self.train_cfg.rpn.get('debug', False)):
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self.rpn_head.debug_imgs = tensor2imgs(img)
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x = self.extract_feat(img)
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losses = self.rpn_head.forward_train(x, img_metas, gt_bboxes, None,
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gt_bboxes_ignore)
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return losses
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def simple_test(self, img, img_metas, rescale=False):
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"""Test function without test time augmentation.
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Args:
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imgs (list[torch.Tensor]): List of multiple images
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img_metas (list[dict]): List of image information.
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rescale (bool, optional): Whether to rescale the results.
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Defaults to False.
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Returns:
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list[np.ndarray]: proposals
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"""
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x = self.extract_feat(img)
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# get origin input shape to onnx dynamic input shape
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if torch.onnx.is_in_onnx_export():
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img_shape = torch._shape_as_tensor(img)[2:]
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img_metas[0]['img_shape_for_onnx'] = img_shape
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proposal_list = self.rpn_head.simple_test_rpn(x, img_metas)
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if rescale:
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for proposals, meta in zip(proposal_list, img_metas):
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proposals[:, :4] /= proposals.new_tensor(meta['scale_factor'])
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if torch.onnx.is_in_onnx_export():
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return proposal_list
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return [proposal.cpu().numpy() for proposal in proposal_list]
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def aug_test(self, imgs, img_metas, rescale=False):
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"""Test function with test time augmentation.
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Args:
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imgs (list[torch.Tensor]): List of multiple images
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img_metas (list[dict]): List of image information.
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rescale (bool, optional): Whether to rescale the results.
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Defaults to False.
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Returns:
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list[np.ndarray]: proposals
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"""
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proposal_list = self.rpn_head.aug_test_rpn(
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self.extract_feats(imgs), img_metas)
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if not rescale:
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for proposals, img_meta in zip(proposal_list, img_metas[0]):
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img_shape = img_meta['img_shape']
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scale_factor = img_meta['scale_factor']
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flip = img_meta['flip']
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flip_direction = img_meta['flip_direction']
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proposals[:, :4] = bbox_mapping(proposals[:, :4], img_shape,
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scale_factor, flip,
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flip_direction)
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return [proposal.cpu().numpy() for proposal in proposal_list]
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def show_result(self, data, result, top_k=20, **kwargs):
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"""Show RPN proposals on the image.
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Args:
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data (str or np.ndarray): Image filename or loaded image.
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result (Tensor or tuple): The results to draw over `img`
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bbox_result or (bbox_result, segm_result).
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top_k (int): Plot the first k bboxes only
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if set positive. Default: 20
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Returns:
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np.ndarray: The image with bboxes drawn on it.
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"""
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if kwargs is not None:
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kwargs.pop('score_thr', None)
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kwargs.pop('text_color', None)
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kwargs['colors'] = kwargs.pop('bbox_color', 'green')
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mmcv.imshow_bboxes(data, result, top_k=top_k, **kwargs)
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