mirror of
https://github.com/open-mmlab/mmdeploy.git
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308e28fcb0
* support cascade (mask) rcnn * fix docstring * support SwinTransformer * move dense_head support to this branch * fix function names * fix part of uts of mmdet * fix for mmdet ut * fix det model cfg for ut * fix test_object_detection.py * fix mmdet object_detection_model.py * fix mmdet yolov3 ort ut * fix part of uts * fix cascade bbox head ut * fix cascade bbox head ut * remove useless ssd ncnn test * fix ncnn wrapper * fix openvino ut for reppoint head * fix openvino cascade mask rcnn * sync codes * support roll * remove unused pad * fix yolox * fix isort * fix lint * fix flake8 * reply for comments and fix failed ut * fix sdk_export in dump_info * fix temp hidden xlsx bugs * fix mmdet regression test * fix lint * fix timer * fix timecount side-effect * adapt profile.py for mmdet 2.0 * hardcode report.txt for T4 benchmark test: temp version * fix no-visualizer case * fix backend_model * fix android build * adapt new mmdet 2.0 0825 * fix new 2.0 * fix test_mmdet_structures * fix test_object_detection * fix codebase import * fix ut * fix all mmdet uts * fix det * fix mmdet trt * fix ncnn onnx optimize
65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import numpy as np
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import torch
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from mmengine import Config
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from mmdeploy.codebase import import_codebase
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from mmdeploy.codebase.mmdet import (clip_bboxes, get_post_processing_params,
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pad_with_value,
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pad_with_value_if_necessary)
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from mmdeploy.utils import Codebase
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import_codebase(Codebase.MMDET)
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def test_clip_bboxes():
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x1 = torch.rand(3, 2) * 224
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y1 = torch.rand(3, 2) * 224
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x2 = x1 * 2
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y2 = y1 * 2
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outs = clip_bboxes(x1, y1, x2, y2, [224, 224])
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for out in outs:
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assert int(out.max()) <= 224
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def test_pad_with_value():
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x = torch.rand(3, 2)
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padded_x = pad_with_value(x, pad_dim=1, pad_size=4, pad_value=0)
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assert np.allclose(
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padded_x.shape, torch.Size([3, 6]), rtol=1e-03, atol=1e-05)
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assert np.allclose(padded_x.sum(), x.sum(), rtol=1e-03, atol=1e-05)
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def test_pad_with_value_if_necessary():
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x = torch.rand(3, 2)
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padded_x = pad_with_value_if_necessary(
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x, pad_dim=1, pad_size=4, pad_value=0)
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assert np.allclose(
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padded_x.shape, torch.Size([3, 2]), rtol=1e-03, atol=1e-05)
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assert np.allclose(padded_x.sum(), x.sum(), rtol=1e-03, atol=1e-05)
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config_with_mmdet_params = Config(
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dict(
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codebase_config=dict(
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type='mmdet',
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task='ObjectDetection',
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post_processing=dict(
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score_threshold=0.05,
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iou_threshold=0.5,
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max_output_boxes_per_class=200,
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pre_top_k=-1,
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keep_top_k=100,
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background_label_id=-1,
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))))
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def test_get_mmdet_params():
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assert get_post_processing_params(config_with_mmdet_params) == dict(
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score_threshold=0.05,
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iou_threshold=0.5,
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max_output_boxes_per_class=200,
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pre_top_k=-1,
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keep_top_k=100,
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background_label_id=-1)
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