OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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70 lines
2.4 KiB
70 lines
2.4 KiB
# 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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