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cbdd98a1e1
* add fcenet * fix linting and code style * fcenet finetune * Update transforms.py * Update fcenet_r50dcnv2_fpn_1500e_ctw1500.py * Update fcenet_targets.py * Update fce_loss.py * fix * add readme * fix config * Update fcenet_r50dcnv2_fpn_1500e_ctw1500.py * fix * fix readme * fix readme * Update test_loss.py Co-authored-by: Hongbin Sun <hongbin306@gmail.com>
29 lines
908 B
Python
29 lines
908 B
Python
import numpy as np
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import torch
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def test_db_boxes_from_bitmaps():
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"""Test the boxes_from_bitmaps function in db_decoder."""
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from mmocr.models.textdet.postprocess.wrapper import db_decode
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pred = np.array([[[0.8, 0.8, 0.8, 0.8, 0], [0.8, 0.8, 0.8, 0.8, 0],
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[0.8, 0.8, 0.8, 0.8, 0], [0.8, 0.8, 0.8, 0.8, 0],
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[0.8, 0.8, 0.8, 0.8, 0]]])
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preds = torch.FloatTensor(pred).requires_grad_(True)
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boxes = db_decode(preds, text_repr_type='quad', min_text_width=0)
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assert len(boxes) == 1
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def test_fcenet_decode():
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from mmocr.models.textdet.postprocess.wrapper import fcenet_decode
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k = 5
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preds = []
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preds.append(torch.randn(1, 4, 40, 40))
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preds.append(torch.randn(1, 4 * k + 2, 40, 40))
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boundaries = fcenet_decode(
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preds=preds, fourier_degree=k, reconstr_points=50, scale=1)
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assert isinstance(boundaries, list)
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