mirror of https://github.com/open-mmlab/mmocr.git
152 lines
5.3 KiB
Python
152 lines
5.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import math
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import pytest
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import torch
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from mmocr.models.textrecog.decoders import (ABILanguageDecoder,
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ABIVisionDecoder, BaseDecoder,
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MasterDecoder, NRTRDecoder,
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ParallelSARDecoder,
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ParallelSARDecoderWithBS,
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SequentialSARDecoder)
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from mmocr.models.textrecog.decoders.sar_decoder_with_bs import DecodeNode
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def _create_dummy_input():
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feat = torch.rand(1, 512, 4, 40)
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out_enc = torch.rand(1, 512)
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tgt_dict = {'padded_targets': torch.LongTensor([[1, 1, 1, 1, 36]])}
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img_metas = [{'valid_ratio': 1.0}]
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return feat, out_enc, tgt_dict, img_metas
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def test_base_decoder():
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decoder = BaseDecoder()
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with pytest.raises(NotImplementedError):
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decoder.forward_train(None, None, None, None)
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with pytest.raises(NotImplementedError):
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decoder.forward_test(None, None, None)
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def test_parallel_sar_decoder():
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# test parallel sar decoder
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decoder = ParallelSARDecoder(num_classes=37, padding_idx=36, max_seq_len=5)
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decoder.init_weights()
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decoder.train()
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feat, out_enc, tgt_dict, img_metas = _create_dummy_input()
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with pytest.raises(AssertionError):
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decoder(feat, out_enc, tgt_dict, [], True)
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with pytest.raises(AssertionError):
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decoder(feat, out_enc, tgt_dict, img_metas * 2, True)
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out_train = decoder(feat, out_enc, tgt_dict, img_metas, True)
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assert out_train.shape == torch.Size([1, 5, 36])
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out_test = decoder(feat, out_enc, tgt_dict, img_metas, False)
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assert out_test.shape == torch.Size([1, 5, 36])
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def test_sequential_sar_decoder():
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# test parallel sar decoder
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decoder = SequentialSARDecoder(
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num_classes=37, padding_idx=36, max_seq_len=5)
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decoder.init_weights()
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decoder.train()
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feat, out_enc, tgt_dict, img_metas = _create_dummy_input()
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with pytest.raises(AssertionError):
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decoder(feat, out_enc, tgt_dict, [])
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with pytest.raises(AssertionError):
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decoder(feat, out_enc, tgt_dict, img_metas * 2)
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out_train = decoder(feat, out_enc, tgt_dict, img_metas, True)
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assert out_train.shape == torch.Size([1, 5, 36])
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out_test = decoder(feat, out_enc, tgt_dict, img_metas, False)
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assert out_test.shape == torch.Size([1, 5, 36])
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def test_parallel_sar_decoder_with_beam_search():
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with pytest.raises(AssertionError):
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ParallelSARDecoderWithBS(beam_width='beam')
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with pytest.raises(AssertionError):
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ParallelSARDecoderWithBS(beam_width=0)
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feat, out_enc, tgt_dict, img_metas = _create_dummy_input()
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decoder = ParallelSARDecoderWithBS(
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beam_width=1, num_classes=37, padding_idx=36, max_seq_len=5)
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decoder.init_weights()
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decoder.train()
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with pytest.raises(AssertionError):
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decoder(feat, out_enc, tgt_dict, [])
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with pytest.raises(AssertionError):
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decoder(feat, out_enc, tgt_dict, img_metas * 2)
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out_test = decoder(feat, out_enc, tgt_dict, img_metas, train_mode=False)
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assert out_test.shape == torch.Size([1, 5, 36])
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# test decodenode
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with pytest.raises(AssertionError):
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DecodeNode(1, 1)
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with pytest.raises(AssertionError):
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DecodeNode([1, 2], ['4', '3'])
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with pytest.raises(AssertionError):
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DecodeNode([1, 2], [0.5])
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decode_node = DecodeNode([1, 2], [0.7, 0.8])
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assert math.isclose(decode_node.eval(), 1.5)
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def test_transformer_decoder():
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decoder = NRTRDecoder(num_classes=37, padding_idx=36, max_seq_len=5)
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decoder.init_weights()
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decoder.train()
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out_enc = torch.rand(1, 25, 512)
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tgt_dict = {'padded_targets': torch.LongTensor([[1, 1, 1, 1, 36]])}
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img_metas = [{'valid_ratio': 1.0}]
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tgt_dict['padded_targets'] = tgt_dict['padded_targets']
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out_train = decoder(None, out_enc, tgt_dict, img_metas, True)
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assert out_train.shape == torch.Size([1, 5, 36])
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out_test = decoder(None, out_enc, tgt_dict, img_metas, False)
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assert out_test.shape == torch.Size([1, 5, 36])
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def test_abi_language_decoder():
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decoder = ABILanguageDecoder(max_seq_len=25)
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logits = torch.randn(2, 25, 90)
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result = decoder(
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feat=None, out_enc=logits, targets_dict=None, img_metas=None)
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assert result['feature'].shape == torch.Size([2, 25, 512])
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assert result['logits'].shape == torch.Size([2, 25, 90])
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def test_abi_vision_decoder():
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model = ABIVisionDecoder(
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in_channels=128, num_channels=16, max_seq_len=10, use_result=None)
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x = torch.randn(2, 128, 8, 32)
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result = model(x, None)
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assert result['feature'].shape == torch.Size([2, 10, 128])
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assert result['logits'].shape == torch.Size([2, 10, 90])
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assert result['attn_scores'].shape == torch.Size([2, 10, 8, 32])
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def test_master_decoder():
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model = MasterDecoder(
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start_idx=0,
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padding_idx=36,
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num_classes=37,
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d_model=64,
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n_head=2,
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max_seq_len=5)
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feat = torch.randn(1, 64, 6, 40)
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tgt_dict = {'padded_targets': torch.LongTensor([[0, 1, 1, 1, 36]])}
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result = model(feat, None, tgt_dict)
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assert result.shape == torch.Size([1, 5, 37])
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result = model.forward_test(feat, None, None)
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assert result.shape == torch.Size([1, 5, 37])
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