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add unit test for textsnake (#61)

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Hongbin Sun 2021-04-13 21:59:19 +08:00 committed by GitHub
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1 changed files with 47 additions and 46 deletions
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tests/test_models/test_detector.py
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@ -316,54 +316,55 @@ def test_dbnet(cfg_file):
detector.show_result(img, results) detector.show_result(img, results)
# @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda') @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')
# @pytest.mark.parametrize( @pytest.mark.parametrize(
# 'cfg_file', ['textdet/textsnake/' 'cfg_file',
# 'textsnake_r50_fpn_unet_1200e_ctw1500.py']) ['textdet/textsnake/'
# def test_textsnake(cfg_file): 'textsnake_r50_fpn_unet_1200e_ctw1500.py'])
# model = _get_detector_cfg(cfg_file) def test_textsnake(cfg_file):
# model['pretrained'] = None model = _get_detector_cfg(cfg_file)
# model['backbone']['norm_cfg']['type'] = 'BN' model['pretrained'] = None
model['backbone']['norm_cfg']['type'] = 'BN'
# from mmocr.models import build_detector from mmocr.models import build_detector
# detector = build_detector(model) detector = build_detector(model)
# detector = detector.cuda() detector = detector.cuda()
# input_shape = (1, 3, 64, 64) input_shape = (1, 3, 64, 64)
# num_kernels = 1 num_kernels = 1
# mm_inputs = _demo_mm_inputs(num_kernels, input_shape) mm_inputs = _demo_mm_inputs(num_kernels, input_shape)
# imgs = mm_inputs.pop('imgs') imgs = mm_inputs.pop('imgs')
# imgs = imgs.cuda() imgs = imgs.cuda()
# img_metas = mm_inputs.pop('img_metas') img_metas = mm_inputs.pop('img_metas')
# gt_text_mask = mm_inputs.pop('gt_text_mask') gt_text_mask = mm_inputs.pop('gt_text_mask')
# gt_center_region_mask = mm_inputs.pop('gt_center_region_mask') gt_center_region_mask = mm_inputs.pop('gt_center_region_mask')
# gt_mask = mm_inputs.pop('gt_mask') gt_mask = mm_inputs.pop('gt_mask')
# gt_radius_map = mm_inputs.pop('gt_radius_map') gt_radius_map = mm_inputs.pop('gt_radius_map')
# gt_sin_map = mm_inputs.pop('gt_sin_map') gt_sin_map = mm_inputs.pop('gt_sin_map')
# gt_cos_map = mm_inputs.pop('gt_cos_map') gt_cos_map = mm_inputs.pop('gt_cos_map')
# # Test forward train # Test forward train
# losses = detector.forward( losses = detector.forward(
# imgs, imgs,
# img_metas, img_metas,
# gt_text_mask=gt_text_mask, gt_text_mask=gt_text_mask,
# gt_center_region_mask=gt_center_region_mask, gt_center_region_mask=gt_center_region_mask,
# gt_mask=gt_mask, gt_mask=gt_mask,
# gt_radius_map=gt_radius_map, gt_radius_map=gt_radius_map,
# gt_sin_map=gt_sin_map, gt_sin_map=gt_sin_map,
# gt_cos_map=gt_cos_map) gt_cos_map=gt_cos_map)
# assert isinstance(losses, dict) assert isinstance(losses, dict)
# # Test forward test # Test forward test
# with torch.no_grad(): # with torch.no_grad():
# img_list = [g[None, :] for g in imgs] # img_list = [g[None, :] for g in imgs]
# batch_results = [] # batch_results = []
# for one_img, one_meta in zip(img_list, img_metas): # for one_img, one_meta in zip(img_list, img_metas):
# result = detector.forward([one_img], [[one_meta]], # result = detector.forward([one_img], [[one_meta]],
# return_loss=False) # return_loss=False)
# batch_results.append(result) # batch_results.append(result)
# # Test show result # Test show result
# results = {'boundary_result': [[0, 0, 1, 0, 1, 1, 0, 1, 0.9]]} results = {'boundary_result': [[0, 0, 1, 0, 1, 1, 0, 1, 0.9]]}
# img = np.random.rand(5, 5) img = np.random.rand(5, 5)
# detector.show_result(img, results) detector.show_result(img, results)