def dpn107(num_classes=1000, pretrained=False, test_time_pool=True):
model = DPN(
num_init_features=128, k_r=200, groups=50,
k_sec=(4, 8, 20, 3), inc_sec=(20, 64, 64, 128),
num_classes=num_classes, test_time_pool=test_time_pool)
if pretrained:
if model_urls['dpn107-extra']:
model.load_state_dict(model_zoo.load_url(model_urls['dpn107-extra']))
elif has_mxnet and os.path.exists('./pretrained/'):
convert_from_mxnet(model, checkpoint_prefix='./pretrained/dpn107-extra')
else:
assert False, "Unable to load a pretrained model"
return model
def dpn68b(num_classes=1000, pretrained=False, test_time_pool=True):
model = DPN(
small=True, num_init_features=10, k_r=128, groups=32,
b=True, k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64),
num_classes=num_classes, test_time_pool=test_time_pool)
if pretrained:
if model_urls['dpn68b-extra']:
model.load_state_dict(model_zoo.load_url(model_urls['dpn68b-extra']))
elif has_mxnet and os.path.exists('./pretrained/'):
convert_from_mxnet(model, checkpoint_prefix='./pretrained/dpn68-extra')
else:
assert False, "Unable to load a pretrained model"
return model
def dpn92(num_classes=1000, pretrained=False, test_time_pool=True, extra=True):
model = DPN(
num_init_features=64, k_r=96, groups=32,
k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128),
num_classes=num_classes, test_time_pool=test_time_pool)
if pretrained:
# there are both imagenet 5k trained, 1k finetuned 'extra' weights
# and normal imagenet 1k trained weights for dpn92
key = 'dpn92'
if extra:
key += '-extra'
if model_urls[key]:
model.load_state_dict(model_zoo.load_url(model_urls[key]))
elif has_mxnet and os.path.exists('./pretrained/'):
convert_from_mxnet(model, checkpoint_prefix='./pretrained/' + key)
else:
assert False, "Unable to load a pretrained model"
return model