Upload folder using huggingface_hub (#3)

- 8011396be3de90a989ea7598db808c8deb851510f847357a7ce7cad16451682d (3f83137cd90613fa2e3116fe931afb5ed353b5d6)

dataset/widerface.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76c08d226ca61ce75f8e5e8056c05e6c7c89aa030080ad455321b41a84f02858
+size 1834228959

layers/functions/prior_box.py

@@ -5,7 +5,7 @@ from math import ceil
 
 
 class PriorBox(object):
-    def __init__(self, cfg, image_size=None, phase='train'):
+    def __init__(self, cfg, image_size=None, phase='test'):
         super(PriorBox, self).__init__()
         self.min_sizes = cfg['min_sizes']
         self.steps = cfg['steps']

models/retinaface.py

@@ -46,10 +46,10 @@ class LandmarkHead(nn.Module):
         return out.view(out.shape[0], -1, 10)
 
 class RetinaFace(nn.Module):
-    def __init__(self, cfg = None, phase = 'train'):
+    def __init__(self, cfg = None, phase = 'test'):
         """
         :param cfg:  Network related settings.
-        :param phase: train or test.
+        :param phase: test or test.
         """
         super(RetinaFace,self).__init__()
         self.phase = phase
@@ -120,7 +120,7 @@ class RetinaFace(nn.Module):
         classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)],dim=1)
         ldm_regressions = torch.cat([self.LandmarkHead[i](feature) for i, feature in enumerate(features)], dim=1)
 
-        if self.phase == 'train':
+        if self.phase == 'test':
             output = (bbox_regressions, classifications, ldm_regressions)
         else:
             output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)

train.py

@@ -14,7 +14,7 @@ import math
 from models.retinaface import RetinaFace
 
 parser = argparse.ArgumentParser(description='Retinaface Training')
-parser.add_argument('--training_dataset', default='./dataset/widerface/widerface/train/label.txt', help='Training dataset directory')
+parser.add_argument('--training_dataset', default='./dataset/widerface/widerface/test/label.txt', help='Training dataset directory')
 parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50')
 parser.add_argument('--num_workers', default=4, type=int, help='Number of workers used in dataloading')
 parser.add_argument('--lr', '--learning-rate', default=1e-3, type=float, help='initial learning rate')
@@ -117,7 +117,7 @@ def train():
             step_index += 1
         lr = adjust_learning_rate(optimizer, gamma, epoch, step_index, iteration, epoch_size)
 
-        # load train data
+        # load test data
         images, targets = next(batch_iterator)
         images = images.cuda()
         targets = [anno.cuda() for anno in targets]

utils/box_utils.py

@@ -208,7 +208,7 @@ def encode_landm(matched, priors, variances):
 # Adapted from https://github.com/Hakuyume/chainer-ssd
 def decode(loc, priors, variances):
     """Decode locations from predictions using priors to undo
-    the encoding we did for offset regression at train time.
+    the encoding we did for offset regression at test time.
     Args:
         loc (tensor): location predictions for loc layers,
             Shape: [num_priors,4]
@@ -228,7 +228,7 @@ def decode(loc, priors, variances):
 
 def decode_landm(pre, priors, variances):
     """Decode landm from predictions using priors to undo
-    the encoding we did for offset regression at train time.
+    the encoding we did for offset regression at test time.
     Args:
         pre (tensor): landm predictions for loc layers,
             Shape: [num_priors,10]