71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import warnings
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import torch
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from ..builder import DETECTORS
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from .single_stage import SingleStageDetector
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@DETECTORS.register_module()
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class DETR(SingleStageDetector):
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r"""Implementation of `DETR: End-to-End Object Detection with
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Transformers <https://arxiv.org/pdf/2005.12872>`_"""
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def __init__(self,
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backbone,
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bbox_head,
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train_cfg=None,
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test_cfg=None,
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pretrained=None,
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init_cfg=None):
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super(DETR, self).__init__(backbone, None, bbox_head, train_cfg,
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test_cfg, pretrained, init_cfg)
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# over-write `forward_dummy` because:
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# the forward of bbox_head requires img_metas
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def forward_dummy(self, img):
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"""Used for computing network flops.
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See `mmdetection/tools/analysis_tools/get_flops.py`
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"""
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warnings.warn('Warning! MultiheadAttention in DETR does not '
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'support flops computation! Do not use the '
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'results in your papers!')
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batch_size, _, height, width = img.shape
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dummy_img_metas = [
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dict(
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batch_input_shape=(height, width),
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img_shape=(height, width, 3)) for _ in range(batch_size)
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]
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x = self.extract_feat(img)
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outs = self.bbox_head(x, dummy_img_metas)
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return outs
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# over-write `onnx_export` because:
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# (1) the forward of bbox_head requires img_metas
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# (2) the different behavior (e.g. construction of `masks`) between
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# torch and ONNX model, during the forward of bbox_head
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def onnx_export(self, img, img_metas):
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"""Test function for exporting to ONNX, without test time augmentation.
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Args:
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img (torch.Tensor): input images.
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img_metas (list[dict]): List of image information.
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Returns:
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tuple[Tensor, Tensor]: dets of shape [N, num_det, 5]
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and class labels of shape [N, num_det].
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"""
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x = self.extract_feat(img)
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# forward of this head requires img_metas
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outs = self.bbox_head.forward_onnx(x, img_metas)
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# get shape as tensor
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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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det_bboxes, det_labels = self.bbox_head.onnx_export(*outs, img_metas)
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return det_bboxes, det_labels
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