55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import mmcv
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import numpy as np
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import torch
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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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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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config_with_mmdet_params = mmcv.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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