def dpn98(num_classes=1000, pretrained=False, test_time_pool=True): model = DPN( num_init_features=96, k_r=160, groups=40, k_sec=(3, 6, 20, 3), inc_sec=(16, 32, 32, 128), num_classes=num_classes, test_time_pool=test_time_pool) if pretrained: if model_urls['dpn98']: model.load_state_dict(model_zoo.load_url(model_urls['dpn98'])) elif has_mxnet and os.path.exists('./pretrained/'): convert_from_mxnet(model, checkpoint_prefix='./pretrained/dpn98') else: assert False, "Unable to load a pretrained model" return model
def dpn68(num_classes=1000, pretrained=False, test_time_pool=True): model = DPN( small=True, num_init_features=10, k_r=128, groups=32, 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['dpn68']: if not os.path.exists(model_urls['dpn68']): raise Exception("File not found: {model_urls['dpn68']}") model.load_state_dict(model_zoo.load_url(model_urls['dpn68'])) elif has_mxnet and os.path.exists('./dpn/pretrained/'): convert_from_mxnet(model, checkpoint_prefix='./dpn/pretrained/dpn68') 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