def main(args):
# build the model from a config file and a checkpoint file
model = init_detector(args.config, args.checkpoint, device=args.device)
# test a single image
model_results = model_inference(model, args.img)
model.show_result(
args.img,
model_results,
win_name='model_results',
show=True,
score_thr=args.score_thr)
url = 'http://' + args.inference_addr + '/predictions/' + args.model_name
with open(args.img, 'rb') as image:
response = requests.post(url, image)
serve_results = response.json()
model.show_result(
args.img,
serve_results,
show=True,
win_name='serve_results',
score_thr=args.score_thr)
assert serve_results.keys() == model_results.keys()
for key in serve_results.keys():
for model_result, serve_result in zip(model_results[key],
serve_results[key]):
if isinstance(model_result[0], (int, float)):
assert np.allclose(model_result, serve_result)
elif isinstance(model_result[0], str):
assert model_result == serve_result
else:
raise TypeError
def main():
parser = ArgumentParser()
parser.add_argument('img_root', type=str, help='Image root path')
parser.add_argument('img_list', type=str, help='Image path list file')
parser.add_argument('config', type=str, help='Config file')
parser.add_argument('checkpoint', type=str, help='Checkpoint file')
parser.add_argument('--score-thr',
type=float,
default=0.5,
help='Bbox score threshold')
parser.add_argument('--out-dir',
type=str,
default='./results',
help='Dir to save '
'visualize images '
'and bbox')
parser.add_argument('--device',
default='cuda:0',
help='Device used for inference.')
args = parser.parse_args()
assert 0 < args.score_thr < 1
# build the model from a config file and a checkpoint file
model = init_detector(args.config, args.checkpoint, device=args.device)
if hasattr(model, 'module'):
model = model.module
if model.cfg.data.test['type'] == 'ConcatDataset':
model.cfg.data.test.pipeline = model.cfg.data.test['datasets'][
0].pipeline
# Start Inference
out_vis_dir = osp.join(args.out_dir, 'out_vis_dir')
mmcv.mkdir_or_exist(out_vis_dir)
out_txt_dir = osp.join(args.out_dir, 'out_txt_dir')
mmcv.mkdir_or_exist(out_txt_dir)
lines = list_from_file(args.img_list)
progressbar = ProgressBar(task_num=len(lines))
for line in lines:
progressbar.update()
img_path = osp.join(args.img_root, line.strip())
if not osp.exists(img_path):
raise FileNotFoundError(img_path)
# Test a single image
result = model_inference(model, img_path)
img_name = osp.basename(img_path)
# save result
save_results(result, out_txt_dir, img_name, score_thr=args.score_thr)
# show result
out_file = osp.join(out_vis_dir, img_name)
kwargs_dict = {
'score_thr': args.score_thr,
'show': False,
'out_file': out_file
}
model.show_result(img_path, result, **kwargs_dict)
print(f'\nInference done, and results saved in {args.out_dir}\n')
def initialize(self, context):
properties = context.system_properties
self.map_location = 'cuda' if torch.cuda.is_available() else 'cpu'
self.device = torch.device(self.map_location + ':' +
str(properties.get('gpu_id')) if torch.cuda.
is_available() else self.map_location)
self.manifest = context.manifest
model_dir = properties.get('model_dir')
serialized_file = self.manifest['model']['serializedFile']
checkpoint = os.path.join(model_dir, serialized_file)
self.config_file = os.path.join(model_dir, 'config.py')
self.model = init_detector(self.config_file, checkpoint, self.device)
self.initialized = True
def main():
parser = ArgumentParser()
parser.add_argument('config', help='Config file.')
parser.add_argument('checkpoint', help='Checkpoint file.')
parser.add_argument('--device',
default='cuda:0',
help='Device used for inference.')
args = parser.parse_args()
# build the model from a config file and a checkpoint file
model = init_detector(args.config, args.checkpoint, device=args.device)
# test a single text
input_sentence = input('Please enter a sentence you want to test: ')
result = text_model_inference(model, input_sentence)
# show the results
for pred_entities in result:
for entity in pred_entities:
print(f'{entity[0]}: {input_sentence[entity[1]:entity[2] + 1]}')
def main():
args = parse_args()
device = torch.device(args.device)
model = init_detector(args.config, args.checkpoint, device=device)
camera = cv2.VideoCapture(args.camera_id)
print('Press "Esc", "q" or "Q" to exit.')
while True:
ret_val, img = camera.read()
result = model_inference(model, img)
ch = cv2.waitKey(1)
if ch == 27 or ch == ord('q') or ch == ord('Q'):
break
model.show_result(img,
result,
score_thr=args.score_thr,
wait_time=1,
show=True)
def main():
parser = ArgumentParser(
description='Convert MMOCR models from pytorch to ONNX')
parser.add_argument('model_config', type=str, help='Config file.')
parser.add_argument('model_ckpt',
type=str,
help='Checkpint file (local or url).')
parser.add_argument('model_type',
type=str,
help='Detection or recognition model to deploy.',
choices=['recog', 'det'])
parser.add_argument('image_path', type=str, help='Input Image file.')
parser.add_argument('--output-file',
type=str,
help='Output file name of the onnx model.',
default='tmp.onnx')
parser.add_argument('--device-id',
default=0,
help='Device used for inference.')
parser.add_argument('--opset-version',
type=int,
help='ONNX opset version, default to 11.',
default=11)
parser.add_argument(
'--verify',
action='store_true',
help='Whether verify the outputs of onnx and pytorch are same.',
default=False)
parser.add_argument('--verbose',
action='store_true',
help='Whether print the computation graph.',
default=False)
parser.add_argument('--show',
action='store_true',
help='Whether visualize final output.',
default=False)
parser.add_argument('--dynamic-export',
action='store_true',
help='Whether dynamically export onnx model.',
default=False)
args = parser.parse_args()
device = torch.device(type='cuda', index=args.device_id)
# build model
model = init_detector(args.model_config, args.model_ckpt, device=device)
if hasattr(model, 'module'):
model = model.module
if model.cfg.data.test['type'] == 'ConcatDataset':
model.cfg.data.test.pipeline = \
model.cfg.data.test['datasets'][0].pipeline
pytorch2onnx(model,
model_type=args.model_type,
output_file=args.output_file,
img_path=args.image_path,
opset_version=args.opset_version,
verify=args.verify,
verbose=args.verbose,
show=args.show,
device_id=args.device_id,
dynamic_export=args.dynamic_export)
def main():
parser = ArgumentParser()
parser.add_argument('img_root_path', type=str, help='Image root path')
parser.add_argument('img_list', type=str, help='Image path list file')
parser.add_argument('config', type=str, help='Config file')
parser.add_argument('checkpoint', type=str, help='Checkpoint file')
parser.add_argument(
'--out-dir', type=str, default='./results', help='Dir to save results')
parser.add_argument(
'--show', action='store_true', help='show image or save')
parser.add_argument(
'--device', default='cuda:0', help='Device used for inference.')
args = parser.parse_args()
# init the logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(args.out_dir, f'{timestamp}.log')
logger = get_root_logger(log_file=log_file, log_level='INFO')
# build the model from a config file and a checkpoint file
model = init_detector(args.config, args.checkpoint, device=args.device)
if hasattr(model, 'module'):
model = model.module
# Start Inference
out_vis_dir = osp.join(args.out_dir, 'out_vis_dir')
mmcv.mkdir_or_exist(out_vis_dir)
correct_vis_dir = osp.join(args.out_dir, 'correct')
mmcv.mkdir_or_exist(correct_vis_dir)
wrong_vis_dir = osp.join(args.out_dir, 'wrong')
mmcv.mkdir_or_exist(wrong_vis_dir)
img_paths, pred_labels, gt_labels = [], [], []
lines = list_from_file(args.img_list)
progressbar = ProgressBar(task_num=len(lines))
num_gt_label = 0
for line in lines:
progressbar.update()
item_list = line.strip().split()
img_file = item_list[0]
gt_label = ''
if len(item_list) >= 2:
gt_label = item_list[1]
num_gt_label += 1
img_path = osp.join(args.img_root_path, img_file)
if not osp.exists(img_path):
raise FileNotFoundError(img_path)
# Test a single image
result = model_inference(model, img_path)
pred_label = result['text']
out_img_name = '_'.join(img_file.split('/'))
out_file = osp.join(out_vis_dir, out_img_name)
kwargs_dict = {
'gt_label': gt_label,
'show': args.show,
'out_file': '' if args.show else out_file
}
model.show_result(img_path, result, **kwargs_dict)
if gt_label != '':
if gt_label == pred_label:
dst_file = osp.join(correct_vis_dir, out_img_name)
else:
dst_file = osp.join(wrong_vis_dir, out_img_name)
shutil.copy(out_file, dst_file)
img_paths.append(img_path)
gt_labels.append(gt_label)
pred_labels.append(pred_label)
# Save results
save_results(img_paths, pred_labels, gt_labels, args.out_dir)
if num_gt_label == len(pred_labels):
# eval
eval_results = eval_ocr_metric(pred_labels, gt_labels)
logger.info('\n' + '-' * 100)
info = ('eval on testset with img_root_path '
f'{args.img_root_path} and img_list {args.img_list}\n')
logger.info(info)
logger.info(eval_results)
print(f'\nInference done, and results saved in {args.out_dir}\n')
def init_detector_skip_ckpt(config, ckpt, device):
return init_detector(config, device=device)
def __init__(self,
det='PANet_IC15',
det_config='',
det_ckpt='',
recog='SEG',
recog_config='',
recog_ckpt='',
kie='',
kie_config='',
kie_ckpt='',
config_dir=os.path.join(str(Path.cwd()), 'configs/'),
device=None,
**kwargs):
textdet_models = {
'DB_r18': {
'config':
'dbnet/dbnet_r18_fpnc_1200e_icdar2015.py',
'ckpt':
'dbnet/'
'dbnet_r18_fpnc_sbn_1200e_icdar2015_20210329-ba3ab597.pth'
},
'DB_r50': {
'config':
'dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py',
'ckpt':
'dbnet/'
'dbnet_r50dcnv2_fpnc_sbn_1200e_icdar2015_20211025-9fe3b590.pth'
},
'DBPP_r50': {
'config':
'dbnetpp/dbnetpp_r50dcnv2_fpnc_1200e_icdar2015.py',
'ckpt':
'dbnet/'
'dbnetpp_r50dcnv2_fpnc_1200e_icdar2015-20220502-d7a76fff.pth'
},
'DRRG': {
'config':
'drrg/drrg_r50_fpn_unet_1200e_ctw1500.py',
'ckpt':
'drrg/drrg_r50_fpn_unet_1200e_ctw1500_20211022-fb30b001.pth'
},
'FCE_IC15': {
'config':
'fcenet/fcenet_r50_fpn_1500e_icdar2015.py',
'ckpt':
'fcenet/fcenet_r50_fpn_1500e_icdar2015_20211022-daefb6ed.pth'
},
'FCE_CTW_DCNv2': {
'config':
'fcenet/fcenet_r50dcnv2_fpn_1500e_ctw1500.py',
'ckpt':
'fcenet/' +
'fcenet_r50dcnv2_fpn_1500e_ctw1500_20211022-e326d7ec.pth'
},
'MaskRCNN_CTW': {
'config':
'maskrcnn/mask_rcnn_r50_fpn_160e_ctw1500.py',
'ckpt':
'maskrcnn/'
'mask_rcnn_r50_fpn_160e_ctw1500_20210219-96497a76.pth'
},
'MaskRCNN_IC15': {
'config':
'maskrcnn/mask_rcnn_r50_fpn_160e_icdar2015.py',
'ckpt':
'maskrcnn/'
'mask_rcnn_r50_fpn_160e_icdar2015_20210219-8eb340a3.pth'
},
'MaskRCNN_IC17': {
'config':
'maskrcnn/mask_rcnn_r50_fpn_160e_icdar2017.py',
'ckpt':
'maskrcnn/'
'mask_rcnn_r50_fpn_160e_icdar2017_20210218-c6ec3ebb.pth'
},
'PANet_CTW': {
'config':
'panet/panet_r18_fpem_ffm_600e_ctw1500.py',
'ckpt':
'panet/'
'panet_r18_fpem_ffm_sbn_600e_ctw1500_20210219-3b3a9aa3.pth'
},
'PANet_IC15': {
'config':
'panet/panet_r18_fpem_ffm_600e_icdar2015.py',
'ckpt':
'panet/'
'panet_r18_fpem_ffm_sbn_600e_icdar2015_20210219-42dbe46a.pth'
},
'PS_CTW': {
'config': 'psenet/psenet_r50_fpnf_600e_ctw1500.py',
'ckpt':
'psenet/psenet_r50_fpnf_600e_ctw1500_20210401-216fed50.pth'
},
'PS_IC15': {
'config':
'psenet/psenet_r50_fpnf_600e_icdar2015.py',
'ckpt':
'psenet/psenet_r50_fpnf_600e_icdar2015_pretrain-eefd8fe6.pth'
},
'TextSnake': {
'config':
'textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py',
'ckpt':
'textsnake/textsnake_r50_fpn_unet_1200e_ctw1500-27f65b64.pth'
},
'Tesseract': {}
}
textrecog_models = {
'CRNN': {
'config': 'crnn/crnn_academic_dataset.py',
'ckpt': 'crnn/crnn_academic-a723a1c5.pth'
},
'SAR': {
'config': 'sar/sar_r31_parallel_decoder_academic.py',
'ckpt': 'sar/sar_r31_parallel_decoder_academic-dba3a4a3.pth'
},
'SAR_CN': {
'config':
'sar/sar_r31_parallel_decoder_chinese.py',
'ckpt':
'sar/sar_r31_parallel_decoder_chineseocr_20210507-b4be8214.pth'
},
'NRTR_1/16-1/8': {
'config': 'nrtr/nrtr_r31_1by16_1by8_academic.py',
'ckpt':
'nrtr/nrtr_r31_1by16_1by8_academic_20211124-f60cebf4.pth'
},
'NRTR_1/8-1/4': {
'config': 'nrtr/nrtr_r31_1by8_1by4_academic.py',
'ckpt':
'nrtr/nrtr_r31_1by8_1by4_academic_20211123-e1fdb322.pth'
},
'RobustScanner': {
'config': 'robust_scanner/robustscanner_r31_academic.py',
'ckpt': 'robustscanner/robustscanner_r31_academic-5f05874f.pth'
},
'SATRN': {
'config': 'satrn/satrn_academic.py',
'ckpt': 'satrn/satrn_academic_20211009-cb8b1580.pth'
},
'SATRN_sm': {
'config': 'satrn/satrn_small.py',
'ckpt': 'satrn/satrn_small_20211009-2cf13355.pth'
},
'ABINet': {
'config': 'abinet/abinet_academic.py',
'ckpt': 'abinet/abinet_academic-f718abf6.pth'
},
'SEG': {
'config': 'seg/seg_r31_1by16_fpnocr_academic.py',
'ckpt': 'seg/seg_r31_1by16_fpnocr_academic-72235b11.pth'
},
'CRNN_TPS': {
'config': 'tps/crnn_tps_academic_dataset.py',
'ckpt': 'tps/crnn_tps_academic_dataset_20210510-d221a905.pth'
},
'Tesseract': {},
'MASTER': {
'config': 'master/master_academic.py',
'ckpt': 'master/master_r31_12e_ST_MJ_SA-787edd36.pth'
}
}
kie_models = {
'SDMGR': {
'config': 'sdmgr/sdmgr_unet16_60e_wildreceipt.py',
'ckpt':
'sdmgr/sdmgr_unet16_60e_wildreceipt_20210520-7489e6de.pth'
}
}
self.td = det
self.tr = recog
self.kie = kie
self.device = device
if self.device is None:
self.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
# Check if the det/recog model choice is valid
if self.td and self.td not in textdet_models:
raise ValueError(self.td,
'is not a supported text detection algorthm')
elif self.tr and self.tr not in textrecog_models:
raise ValueError(self.tr,
'is not a supported text recognition algorithm')
elif self.kie:
if self.kie not in kie_models:
raise ValueError(
self.kie, 'is not a supported key information extraction'
' algorithm')
elif not (self.td and self.tr):
raise NotImplementedError(
self.kie, 'has to run together'
' with text detection and recognition algorithms.')
self.detect_model = None
if self.td and self.td == 'Tesseract':
if tesserocr is None:
raise ImportError('Please install tesserocr first. '
'Check out the installation guide at '
'https://github.com/sirfz/tesserocr')
self.detect_model = 'Tesseract_det'
elif self.td:
# Build detection model
if not det_config:
det_config = os.path.join(config_dir, 'textdet/',
textdet_models[self.td]['config'])
if not det_ckpt:
det_ckpt = 'https://download.openmmlab.com/mmocr/textdet/' + \
textdet_models[self.td]['ckpt']
self.detect_model = init_detector(det_config,
det_ckpt,
device=self.device)
self.detect_model = revert_sync_batchnorm(self.detect_model)
self.recog_model = None
if self.tr and self.tr == 'Tesseract':
if tesserocr is None:
raise ImportError('Please install tesserocr first. '
'Check out the installation guide at '
'https://github.com/sirfz/tesserocr')
self.recog_model = 'Tesseract_recog'
elif self.tr:
# Build recognition model
if not recog_config:
recog_config = os.path.join(
config_dir, 'textrecog/',
textrecog_models[self.tr]['config'])
if not recog_ckpt:
recog_ckpt = 'https://download.openmmlab.com/mmocr/' + \
'textrecog/' + textrecog_models[self.tr]['ckpt']
self.recog_model = init_detector(recog_config,
recog_ckpt,
device=self.device)
self.recog_model = revert_sync_batchnorm(self.recog_model)
self.kie_model = None
if self.kie:
# Build key information extraction model
if not kie_config:
kie_config = os.path.join(config_dir, 'kie/',
kie_models[self.kie]['config'])
if not kie_ckpt:
kie_ckpt = 'https://download.openmmlab.com/mmocr/' + \
'kie/' + kie_models[self.kie]['ckpt']
kie_cfg = Config.fromfile(kie_config)
self.kie_model = build_detector(kie_cfg.model,
test_cfg=kie_cfg.get('test_cfg'))
self.kie_model = revert_sync_batchnorm(self.kie_model)
self.kie_model.cfg = kie_cfg
load_checkpoint(self.kie_model, kie_ckpt, map_location=self.device)
# Attribute check
for model in list(filter(None, [self.recog_model, self.detect_model])):
if hasattr(model, 'module'):
model = model.module