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51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os.path as osp
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import tempfile
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from unittest import TestCase
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import torch
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from mmocr.models.textrecog.decoders import ABIVisionDecoder
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from mmocr.testing import create_dummy_dict_file
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class TestABIVisionDecoder(TestCase):
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def test_forward(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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dict_file = osp.join(tmp_dir, 'fake_chars.txt')
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create_dummy_dict_file(dict_file)
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# test diction cfg
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dict_cfg = dict(
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type='Dictionary',
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dict_file=dict_file,
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with_start=True,
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with_end=True,
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same_start_end=False,
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with_padding=False,
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with_unknown=False)
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decoder = ABIVisionDecoder(
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dict_cfg, in_channels=32, num_channels=16, max_seq_len=10)
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# training
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out_enc = torch.randn(2, 32, 8, 32)
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result = decoder.forward_train(None, out_enc)
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self.assertIn('feature', result)
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self.assertIn('logits', result)
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self.assertIn('attn_scores', result)
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self.assertEqual(result['feature'].shape, torch.Size([2, 10, 32]))
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self.assertEqual(result['logits'].shape, torch.Size([2, 10, 38]))
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self.assertEqual(result['attn_scores'].shape,
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torch.Size([2, 10, 8, 32]))
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# testing
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result = decoder.forward_test(None, out_enc)
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self.assertIn('feature', result)
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self.assertIn('logits', result)
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self.assertIn('attn_scores', result)
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self.assertEqual(result['feature'].shape, torch.Size([2, 10, 32]))
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self.assertEqual(result['logits'].shape, torch.Size([2, 10, 38]))
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self.assertEqual(result['attn_scores'].shape,
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torch.Size([2, 10, 8, 32]))
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