def mnasnet_small(pretrained=False, **kwargs): """ MNASNet Small, depth multiplier of 1.0. """ model = _gen_mnasnet_small(1.0, **kwargs) if pretrained: model.load_state_dict( load_state_dict_from_url(model_urls['mnasnet_small'])) return model
def spnasnet_100(pretrained=False, **kwargs): """ Single-Path NAS Pixel1""" model = _gen_spnasnet(1.0, **kwargs) if pretrained: model.load_state_dict( load_state_dict_from_url(model_urls['spnasnet_100'])) return model
def mnasnet_140(pretrained=False, **kwargs): """ MNASNet B1, depth multiplier of 1.4 """ model = _gen_mnasnet_b1(1.4, **kwargs) if pretrained: model.load_state_dict( load_state_dict_from_url(model_urls['mnasnet_140'])) return model
def semnasnet_140(pretrained=False, **kwargs): """ MNASNet A1 (w/ SE), depth multiplier of 1.4. """ model = _gen_mnasnet_a1(1.4, **kwargs) if pretrained: model.load_state_dict( load_state_dict_from_url(model_urls['semnasnet_140'])) return model
def mnasnet_075(pretrained=False, **kwargs): """ MNASNet B1, depth multiplier of 0.75. """ model = _gen_mnasnet_b1(0.75, **kwargs) if pretrained: model.load_state_dict( load_state_dict_from_url(model_urls['mnasnet_075'])) return model
def tf_efficientnet_b3(pretrained=False, **kwargs): """ EfficientNet-B3. Tensorflow compatible variant """ kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT kwargs['padding_same'] = True model = _gen_efficientnet(channel_multiplier=1.2, depth_multiplier=1.4, **kwargs) if pretrained: model.load_state_dict(load_state_dict_from_url(model_urls['tf_efficientnet_b3'])) return model
def tflite_semnasnet_100(pretrained=False, **kwargs): """ MNASNet A1, depth multiplier of 1.0. """ # these two args are for compat with tflite pretrained weights kwargs['folded_bn'] = True kwargs['padding_same'] = True model = _gen_mnasnet_a1(1.0, **kwargs) if pretrained: model.load_state_dict(load_state_dict_from_url(model_urls['tflite_semnasnet_100'])) return model
def fbnetc_100(pretrained=False, **kwargs): """ FBNet-C """ if pretrained: # pretrained model trained with non-default BN epsilon kwargs['bn_eps'] = 1e-3 model = _gen_fbnetc(1.0, **kwargs) if pretrained: model.load_state_dict( load_state_dict_from_url(model_urls['fbnetc_100'])) return model
def mobilenetv3_100(num_classes, in_chans=3, pretrained=False, **kwargs): """ MobileNet V3 """ model = _gen_mobilenet_v3(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs) if pretrained: model.load_state_dict( load_state_dict_from_url(model_urls['mobilenetv3_100'])) return model
def efficientnet_b0(pretrained=False, **kwargs): """ EfficientNet-B0 model Args: pretrained (bool): If True, returns a model pre-trained on ImageNet-1K """ model = _gen_efficientnet(channel_multiplier=1.0, depth_multiplier=1.0, **kwargs) if pretrained: model.load_state_dict(load_state_dict_from_url(model_urls['efficientnet_b0'])) return model
def mobilenetv3_100(pretrained=False, **kwargs): """ Constructs a MobileNet-V3 100 (depth_multiplier == 1.0) model Args: pretrained (bool): If True, returns a model pre-trained on ImageNet-1K """ if pretrained and 'bn_eps' not in kwargs: # pretrained model trained with non-default BN epsilon kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT model = _gen_mobilenet_v3(1.0, **kwargs) if pretrained: model.load_state_dict(load_state_dict_from_url(model_urls['mobilenetv3_100'])) return model