Upload folder using huggingface_hub
#3
by
datnguyentien204
- opened
- dataset/widerface.zip +3 -0
- layers/functions/prior_box.py +1 -1
- models/retinaface.py +3 -3
- train.py +2 -2
- utils/box_utils.py +2 -2
dataset/widerface.zip
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:76c08d226ca61ce75f8e5e8056c05e6c7c89aa030080ad455321b41a84f02858
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size 1834228959
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layers/functions/prior_box.py
CHANGED
@@ -5,7 +5,7 @@ from math import ceil
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class PriorBox(object):
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def __init__(self, cfg, image_size=None, phase='
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super(PriorBox, self).__init__()
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self.min_sizes = cfg['min_sizes']
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self.steps = cfg['steps']
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class PriorBox(object):
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def __init__(self, cfg, image_size=None, phase='test'):
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super(PriorBox, self).__init__()
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self.min_sizes = cfg['min_sizes']
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self.steps = cfg['steps']
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models/retinaface.py
CHANGED
@@ -46,10 +46,10 @@ class LandmarkHead(nn.Module):
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return out.view(out.shape[0], -1, 10)
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class RetinaFace(nn.Module):
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def __init__(self, cfg = None, phase = '
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"""
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:param cfg: Network related settings.
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:param phase:
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"""
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super(RetinaFace,self).__init__()
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self.phase = phase
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@@ -120,7 +120,7 @@ class RetinaFace(nn.Module):
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classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)],dim=1)
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ldm_regressions = torch.cat([self.LandmarkHead[i](feature) for i, feature in enumerate(features)], dim=1)
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if self.phase == '
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output = (bbox_regressions, classifications, ldm_regressions)
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else:
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output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)
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return out.view(out.shape[0], -1, 10)
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class RetinaFace(nn.Module):
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def __init__(self, cfg = None, phase = 'test'):
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"""
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:param cfg: Network related settings.
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:param phase: test or test.
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"""
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super(RetinaFace,self).__init__()
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self.phase = phase
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classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)],dim=1)
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ldm_regressions = torch.cat([self.LandmarkHead[i](feature) for i, feature in enumerate(features)], dim=1)
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if self.phase == 'test':
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output = (bbox_regressions, classifications, ldm_regressions)
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else:
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output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)
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train.py
CHANGED
@@ -14,7 +14,7 @@ import math
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from models.retinaface import RetinaFace
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parser = argparse.ArgumentParser(description='Retinaface Training')
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parser.add_argument('--training_dataset', default='./dataset/widerface/widerface/
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parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50')
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parser.add_argument('--num_workers', default=4, type=int, help='Number of workers used in dataloading')
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parser.add_argument('--lr', '--learning-rate', default=1e-3, type=float, help='initial learning rate')
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step_index += 1
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lr = adjust_learning_rate(optimizer, gamma, epoch, step_index, iteration, epoch_size)
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# load
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images, targets = next(batch_iterator)
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images = images.cuda()
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targets = [anno.cuda() for anno in targets]
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from models.retinaface import RetinaFace
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parser = argparse.ArgumentParser(description='Retinaface Training')
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parser.add_argument('--training_dataset', default='./dataset/widerface/widerface/test/label.txt', help='Training dataset directory')
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parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50')
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parser.add_argument('--num_workers', default=4, type=int, help='Number of workers used in dataloading')
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parser.add_argument('--lr', '--learning-rate', default=1e-3, type=float, help='initial learning rate')
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step_index += 1
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lr = adjust_learning_rate(optimizer, gamma, epoch, step_index, iteration, epoch_size)
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# load test data
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images, targets = next(batch_iterator)
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images = images.cuda()
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targets = [anno.cuda() for anno in targets]
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utils/box_utils.py
CHANGED
@@ -208,7 +208,7 @@ def encode_landm(matched, priors, variances):
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# Adapted from https://github.com/Hakuyume/chainer-ssd
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def decode(loc, priors, variances):
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"""Decode locations from predictions using priors to undo
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the encoding we did for offset regression at
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Args:
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loc (tensor): location predictions for loc layers,
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Shape: [num_priors,4]
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def decode_landm(pre, priors, variances):
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"""Decode landm from predictions using priors to undo
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the encoding we did for offset regression at
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Args:
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pre (tensor): landm predictions for loc layers,
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Shape: [num_priors,10]
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# Adapted from https://github.com/Hakuyume/chainer-ssd
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def decode(loc, priors, variances):
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"""Decode locations from predictions using priors to undo
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the encoding we did for offset regression at test time.
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Args:
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loc (tensor): location predictions for loc layers,
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Shape: [num_priors,4]
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def decode_landm(pre, priors, variances):
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"""Decode landm from predictions using priors to undo
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the encoding we did for offset regression at test time.
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Args:
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pre (tensor): landm predictions for loc layers,
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Shape: [num_priors,10]
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