update PP-HGNetV2 (#2993)
cuicheng01
GitHub
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@ -41,7 +41,7 @@ PP-HGNetV2 在 PP-HGNet 上的具体改进点如下：
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- 改进了 PPHGNet 网络 stem 部分，堆叠更多的 2x2 卷积核以学习更丰富的局部特征，使用更小的通道数以提升大分辨率任务如目标检测、语义分割等的推理速度；
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- 替换了 PP-HGNet 中靠后 stage 的较冗余的标准卷积层为 PW + DW5x5 组合，在获得更大感受野的同时网络的参数量更少，且精度可以进一步提升；
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- 增加了 LearnableAffineBlock 模块，其可以在增加极少参数量的同时大幅提升较小模型的精度，且对推理时间无损；
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- 重构了 PP-HGNet 网络的 stage 分布，使其涵盖了从 B0-B7 不同量级的模型，从而满足不同任务的需求。
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- 重构了 PP-HGNet 网络的 stage 分布，使其涵盖了从 B0-B6 不同量级的模型，从而满足不同任务的需求。
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除以上改进点之外，相比 PaddleClas 提供的其他模型，PP-HGNetV2 默认提供了精度更高、泛化能力更强的 [SSLD](https://arxiv.org/abs/2103.05959) 预训练权重，其在下游任务中表现更佳。
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@ -60,8 +60,6 @@ PP-HGNetV2 的精度、速度指标、预训练权重、推理模型权重链接
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| PPHGNetV2_B4 | 83.57 | 96.72 | 1.46 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B4_ssld_stage1_pretrained.pdparams)| [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B4_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNetV2_B4_ssld_infer.tar) |
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| PPHGNetV2_B5 | 84.75 | 97.32 | 2.84 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B5_ssld_stage1_pretrained.pdparams)| [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B5_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNetV2_B5_ssld_infer.tar) |
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| PPHGNetV2_B6 | 86.30 | 97.84 | 5.29 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B6_ssld_stage1_pretrained.pdparams)| [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B6_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNetV2_B6_ssld_infer.tar) |
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| PPHGNetV2_B7 | comming soon | comming soon | 11.06 |comming soon| comming soon | comming soon |
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**备注：**
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@ -25,7 +25,7 @@ from .legendary_models.pp_lcnet import PPLCNet_x0_25, PPLCNet_x0_35, PPLCNet_x0_
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from .legendary_models.pp_lcnet_v2 import PPLCNetV2_small, PPLCNetV2_base, PPLCNetV2_large
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from .legendary_models.esnet import ESNet_x0_25, ESNet_x0_5, ESNet_x0_75, ESNet_x1_0
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from .legendary_models.pp_hgnet import PPHGNet_tiny, PPHGNet_small, PPHGNet_base
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from .legendary_models.pp_hgnet_v2 import PPHGNetV2_B0, PPHGNetV2_B1, PPHGNetV2_B2, PPHGNetV2_B3, PPHGNetV2_B4, PPHGNetV2_B5, PPHGNetV2_B6, PPHGNetV2_B7
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from .legendary_models.pp_hgnet_v2 import PPHGNetV2_B0, PPHGNetV2_B1, PPHGNetV2_B2, PPHGNetV2_B3, PPHGNetV2_B4, PPHGNetV2_B5, PPHGNetV2_B6
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from .model_zoo.resnet_vc import ResNet50_vc
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from .model_zoo.resnext import ResNeXt50_32x4d, ResNeXt50_64x4d, ResNeXt101_32x4d, ResNeXt101_64x4d, ResNeXt152_32x4d, ResNeXt152_64x4d
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@ -38,8 +38,6 @@ MODEL_URLS = {
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B5_ssld_pretrained.pdparams",
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"PPHGNetV2_B6":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B6_ssld_pretrained.pdparams",
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"PPHGNetV2_B7":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B7_ssld_pretrained.pdparams",
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}
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__all__ = list(MODEL_URLS.keys())
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@ -702,30 +700,3 @@ def PPHGNetV2_B6(pretrained=False, use_ssld=False, **kwargs):
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["PPHGNetV2_B6"], use_ssld)
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return model
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def PPHGNetV2_B7(pretrained=False, use_ssld=False, **kwargs):
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"""
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PPHGNetV2_B7
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Args:
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pretrained (bool/str): If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld (bool) Whether using ssld pretrained model when pretrained is True.
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Returns:
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model: nn.Layer. Specific `PPHGNetV2_B7` model depends on args.
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"""
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stage_config = {
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# in_channels, mid_channels, out_channels, num_blocks, is_downsample, light_block, kernel_size, layer_num
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"stage1": [128, 128, 256, 2, False, False, 3, 7],
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"stage2": [256, 256, 512, 4, True, False, 3, 7],
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"stage3": [512, 512, 1024, 12, True, True, 5, 7],
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"stage4": [1024, 1024, 2048, 4, True, True, 5, 7],
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}
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model = PPHGNetV2(
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stem_channels=[3, 64, 128],
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stage_config=stage_config,
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use_lab=False,
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["PPHGNetV2_B7"], use_ssld)
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return model
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