Spaces:
Runtime error
Runtime error
File size: 7,228 Bytes
2366e36 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import json
import os
import os.path as osp
import tempfile
import mmcv
import numpy as np
import pytest
import torch
from mmcv import Config
from mmcv.parallel import MMDataParallel
from mmocr.apis.test import single_gpu_test
from mmocr.datasets import build_dataloader, build_dataset
from mmocr.models import build_detector
from mmocr.utils import check_argument, list_to_file, revert_sync_batchnorm
def build_model(cfg):
model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
model = revert_sync_batchnorm(model)
model = MMDataParallel(model)
return model
def generate_sample_dataloader(cfg, curr_dir, img_prefix='', ann_file=''):
must_keys = ['img_norm_cfg', 'ori_filename', 'img_shape', 'ori_shape']
test_pipeline = cfg.data.test.pipeline
for key in must_keys:
if test_pipeline[1].type == 'MultiRotateAugOCR':
collect_pipeline = test_pipeline[1]['transforms'][-1]
else:
collect_pipeline = test_pipeline[-1]
if 'meta_keys' not in collect_pipeline:
continue
collect_pipeline['meta_keys'].append(key)
img_prefix = osp.join(curr_dir, img_prefix)
ann_file = osp.join(curr_dir, ann_file)
test = copy.deepcopy(cfg.data.test.datasets[0])
test.img_prefix = img_prefix
test.ann_file = ann_file
cfg.data.workers_per_gpu = 0
cfg.data.test.datasets = [test]
dataset = build_dataset(cfg.data.test)
loader_cfg = {
**dict((k, cfg.data[k]) for k in [
'workers_per_gpu', 'samples_per_gpu'
] if k in cfg.data)
}
test_loader_cfg = {
**loader_cfg,
**dict(shuffle=False, drop_last=False),
**cfg.data.get('test_dataloader', {})
}
data_loader = build_dataloader(dataset, **test_loader_cfg)
return data_loader
@pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')
@pytest.mark.parametrize('cfg_file', [
'../configs/textrecog/sar/sar_r31_parallel_decoder_academic.py',
'../configs/textrecog/crnn/crnn_academic_dataset.py',
'../configs/textrecog/seg/seg_r31_1by16_fpnocr_academic.py'
])
def test_single_gpu_test_recog(cfg_file):
curr_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
config_file = os.path.join(curr_dir, cfg_file)
cfg = Config.fromfile(config_file)
model = build_model(cfg)
img_prefix = 'data/ocr_toy_dataset/imgs'
ann_file = 'data/ocr_toy_dataset/label.txt'
data_loader = generate_sample_dataloader(cfg, curr_dir, img_prefix,
ann_file)
with tempfile.TemporaryDirectory() as tmpdirname:
out_dir = osp.join(tmpdirname, 'tmp')
results = single_gpu_test(model, data_loader, out_dir=out_dir)
assert check_argument.is_type_list(results, dict)
@pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')
@pytest.mark.parametrize(
'cfg_file',
['../configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2017.py'])
def test_single_gpu_test_det(cfg_file):
curr_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
config_file = os.path.join(curr_dir, cfg_file)
cfg = Config.fromfile(config_file)
model = build_model(cfg)
img_prefix = 'data/toy_dataset/imgs'
ann_file = 'data/toy_dataset/instances_test.json'
data_loader = generate_sample_dataloader(cfg, curr_dir, img_prefix,
ann_file)
with tempfile.TemporaryDirectory() as tmpdirname:
out_dir = osp.join(tmpdirname, 'tmp')
results = single_gpu_test(model, data_loader, out_dir=out_dir)
assert check_argument.is_type_list(results, dict)
def gene_sdmgr_model_dataloader(cfg, dirname, curr_dir, empty_img=False):
json_obj = {
'file_name':
'1.jpg',
'height':
348,
'width':
348,
'annotations': [{
'box': [114.0, 19.0, 230.0, 19.0, 230.0, 1.0, 114.0, 1.0],
'text':
'CHOEUN',
'label':
1
}]
}
ann_file = osp.join(dirname, 'test.txt')
list_to_file(ann_file, [json.dumps(json_obj, ensure_ascii=False)])
if not empty_img:
img = np.ones((348, 348, 3), dtype=np.uint8)
img_file = osp.join(dirname, '1.jpg')
mmcv.imwrite(img, img_file)
test = copy.deepcopy(cfg.data.test)
test.ann_file = ann_file
test.img_prefix = dirname
test.dict_file = osp.join(curr_dir, 'data/kie_toy_dataset/dict.txt')
cfg.data.workers_per_gpu = 1
cfg.data.test = test
cfg.model.class_list = osp.join(curr_dir,
'data/kie_toy_dataset/class_list.txt')
dataset = build_dataset(cfg.data.test)
loader_cfg = {
**dict((k, cfg.data[k]) for k in [
'workers_per_gpu', 'samples_per_gpu'
] if k in cfg.data)
}
test_loader_cfg = {
**loader_cfg,
**dict(shuffle=False, drop_last=False),
**cfg.data.get('test_dataloader', {})
}
data_loader = build_dataloader(dataset, **test_loader_cfg)
model = build_model(cfg)
return model, data_loader
@pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')
@pytest.mark.parametrize(
'cfg_file', ['../configs/kie/sdmgr/sdmgr_unet16_60e_wildreceipt.py'])
def test_single_gpu_test_kie(cfg_file):
curr_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
config_file = os.path.join(curr_dir, cfg_file)
cfg = Config.fromfile(config_file)
with tempfile.TemporaryDirectory() as tmpdirname:
out_dir = osp.join(tmpdirname, 'tmp')
model, data_loader = gene_sdmgr_model_dataloader(
cfg, out_dir, curr_dir)
results = single_gpu_test(
model, data_loader, out_dir=out_dir, is_kie=True)
assert check_argument.is_type_list(results, dict)
@pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')
@pytest.mark.parametrize(
'cfg_file', ['../configs/kie/sdmgr/sdmgr_novisual_60e_wildreceipt.py'])
def test_single_gpu_test_kie_novisual(cfg_file):
curr_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
config_file = os.path.join(curr_dir, cfg_file)
cfg = Config.fromfile(config_file)
meta_keys = list(cfg.data.test.pipeline[-1]['meta_keys'])
must_keys = ['img_norm_cfg', 'ori_filename', 'img_shape']
for key in must_keys:
meta_keys.append(key)
cfg.data.test.pipeline[-1]['meta_keys'] = tuple(meta_keys)
with tempfile.TemporaryDirectory() as tmpdirname:
out_dir = osp.join(tmpdirname, 'tmp')
model, data_loader = gene_sdmgr_model_dataloader(
cfg, out_dir, curr_dir, empty_img=True)
results = single_gpu_test(
model, data_loader, out_dir=out_dir, is_kie=True)
assert check_argument.is_type_list(results, dict)
model, data_loader = gene_sdmgr_model_dataloader(
cfg, out_dir, curr_dir)
results = single_gpu_test(
model, data_loader, out_dir=out_dir, is_kie=True)
assert check_argument.is_type_list(results, dict)
|