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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import tempfile
import pytest
import torch
from mmocr.models import build_detector
def _create_dummy_vocab_file(vocab_file):
with open(vocab_file, 'w') as fw:
for char in list(map(chr, range(ord('a'), ord('z') + 1))):
fw.write(char + '\n')
def _get_config_module(fname):
"""Load a configuration as a python module."""
from mmcv import Config
config_mod = Config.fromfile(fname)
return config_mod
def _get_detector_cfg(fname):
"""Grab configs necessary to create a detector.
These are deep copied to allow for safe modification of parameters without
influencing other tests.
"""
config = _get_config_module(fname)
model = copy.deepcopy(config.model)
return model
@pytest.mark.parametrize(
'cfg_file', ['configs/ner/bert_softmax/bert_softmax_cluener_18e.py'])
def test_bert_softmax(cfg_file):
# prepare data
texts = ['中'] * 47
img = [31] * 47
labels = [31] * 128
input_ids = [0] * 128
attention_mask = [0] * 128
token_type_ids = [0] * 128
img_metas = {
'texts': texts,
'labels': torch.tensor(labels).unsqueeze(0),
'img': img,
'input_ids': torch.tensor(input_ids).unsqueeze(0),
'attention_masks': torch.tensor(attention_mask).unsqueeze(0),
'token_type_ids': torch.tensor(token_type_ids).unsqueeze(0)
}
# create dummy data
tmp_dir = tempfile.TemporaryDirectory()
vocab_file = osp.join(tmp_dir.name, 'fake_vocab.txt')
_create_dummy_vocab_file(vocab_file)
model = _get_detector_cfg(cfg_file)
model['label_convertor']['vocab_file'] = vocab_file
detector = build_detector(model)
losses = detector.forward(img, img_metas)
assert isinstance(losses, dict)
model['loss']['type'] = 'MaskedFocalLoss'
detector = build_detector(model)
losses = detector.forward(img, img_metas)
assert isinstance(losses, dict)
tmp_dir.cleanup()
# Test forward test
with torch.no_grad():
batch_results = []
result = detector.forward(None, img_metas, return_loss=False)
batch_results.append(result)
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