diff --git a/ppcls/arch/backbone/model_zoo/peleenet.py b/ppcls/arch/backbone/model_zoo/peleenet.py index 26af5da17..bfeb70bb5 100644 --- a/ppcls/arch/backbone/model_zoo/peleenet.py +++ b/ppcls/arch/backbone/model_zoo/peleenet.py @@ -134,7 +134,7 @@ class BasicConv2d(nn.Layer): return x -class PeleeNet(nn.Layer): +class PeleeNetDY(nn.Layer): r"""PeleeNet model class, based on `"Densely Connected Convolutional Networks" and "Pelee: A Real-Time Object Detection System on Mobile Devices" ` @@ -153,7 +153,7 @@ class PeleeNet(nn.Layer): num_init_features=32, bottleneck_width=[1, 2, 4, 4], drop_rate=0.05, class_num=1000): - super(PeleeNet, self).__init__() + super(PeleeNetDY, self).__init__() self.features = nn.Sequential(*[ ('stemblock', _StemBlock(3, num_init_features)), @@ -233,7 +233,7 @@ def _load_pretrained(pretrained, model, model_url, use_ssld): ) -def peleenet(pretrained=False, use_ssld=False, **kwargs): - model = PeleeNet(**kwargs) +def PeleeNet(pretrained=False, use_ssld=False, **kwargs): + model = PeleeNetDY(**kwargs) _load_pretrained(pretrained, model, MODEL_URLS["peleenet"], use_ssld) return model diff --git a/ppcls/configs/ImageNet/PeleeNet/PeleeNet.yaml b/ppcls/configs/ImageNet/PeleeNet/PeleeNet.yaml index e84ea1db1..648f97040 100644 --- a/ppcls/configs/ImageNet/PeleeNet/PeleeNet.yaml +++ b/ppcls/configs/ImageNet/PeleeNet/PeleeNet.yaml @@ -65,7 +65,7 @@ DataLoader: sampler: name: DistributedBatchSampler - batch_size: 256 # for 2 cards + batch_size: 128 drop_last: False shuffle: True loader: