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dengdan / seglink

An Implementation of the seglink alogrithm in paper Detecting Oriented Text in Natural Images by Linking Segments
GNU General Public License v3.0
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Slow evaluation speed... #14

Closed JarveeLee closed 7 years ago

JarveeLee commented 7 years ago

It is me again.... I tried to speed up evaluation by changing eval_seglink.py 's batch_size to 8 instead of 1 , which took me 40 mins to evaluate my own entire dataset of about 14000 images config.init_config(image_shape, batch_size = 8, seg_conf_threshold = FLAGS.seg_conf_threshold, link_conf_threshold = FLAGS.link_conf_threshold, train_with_ignored = FLAGS.train_with_ignored, seg_loc_loss_weight = FLAGS.seg_loc_loss_weight, link_cls_loss_weight = FLAGS.link_cls_loss_weight, )

but changing to 8 will report error ValueError: slice index 1 of dimension 0 out of bounds. for 'evaluation_512x512/strided_slice_4' (op: 'StridedSlice') with input shapes: [1,5460], [2], [2], [2] and with computed input tensors: input[1] = <1 0>, input[2] = <2 0>, input[3] = <1 1>.

What does it mean....? Can only evaluate with batch_size more than one ?

dengdan commented 7 years ago

Yes, only one image per image supported in testing. I put the decode process into graph, and the number of decoded bboxes varies from image to image, making it inconvenient to decode in batches.