本文提出递归门控卷积(gnConv)，它通过门控卷积核递归设计执行高效、可扩展和平移等变的高阶空间交互，即插即用来改进各种视觉Transformer和基于CNN的模型，并提出新的视觉骨干网络家族：HorNet
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超越 ConvNeXt、Swin(涨点神器): 利用递归门控卷积的高阶空间交互网络
HorNet:新的空间交互模块
1. 摘要
本文提出递归门控卷积(gnConv)，它通过门控卷积核递归设计执行高效、可扩展和平移等变的高阶空间交互，即插即用来改进各种视觉Transformer和基于CNN的模型，并提出新的视觉骨干网络家族：HorNet
2.设计缘由
1、利用点乘积的自我注意在视觉任务中的有效性尚未从高阶空间交互的方面进行分析；
2、由于非线性的原因，深度模型存在复杂的计算和经常高阶两个空间位置之间的交互，自注意力和其他动态网络的成功表明，显式和高阶空间交互引入的设计有利于提高视觉模型的建模能力。
3、视觉建模的基本操作(例如自注意力中的点乘)趋势表明，可以通过增加空间交互的次数来提高网络容量。下图展示了普通卷积、注意力卷积、Transformer block以及本文的递归模块。顺序依次为a、b、c、d。 
3.核心架构：递归门控卷积
门控卷积结构如下图所示，括号中表示输出通道数。从图中可以看出，门控卷积就是首先通过两个卷积层来调整特征通道数。接着，将深度可分离卷积的输出特征沿着特征分成多块，每一块与前一块交互的特征进一步进行逐元素相乘的方式进行交互，最终得到输出特征。这里递归就是不断地进行逐元素相乘操作，通过这种递归方式特征越在后面的特征高阶信息保存越多，这样在高阶中特征交互就会足够多。
4.代码复现
本项目基于Paddleclas对HorNet进行复现，对Paddleclas感兴趣的可以去GITHUB了解一下。
代码位置: Hornet.py和PaddleClas/ppcls/arch/backbone/modelzoo/hornet.py
核心代码展示(gnConv部分)
class gnconv(nn.Layer): def __init__(self, dim, order=5, gflayer=None, h=14, w=8, s=1.0): super().__init__() self.order = order self.dims = [dim // 2 ** i for i in range(order)] self.dims.reverse() self.proj_in = nn.Conv2D(dim, 2*dim, 1) if gflayer is None: #这里就是定义上图中的深度可分离卷积 self.dwconv = get_dwconv(sum(self.dims), 7, True) else: self.dwconv = gflayer(sum(self.dims), h=h, w=w) self.proj_out = nn.Conv2D(dim, dim, 1) #这里就是第一个映射层 self.pws = nn.LayerList( [nn.Conv2D(self.dims[i], self.dims[i+1], 1) for i in range(order-1)] ) self.scale = s #print('[gnconv]', order, 'order with dims=', self.dims, 'scale=%.4f'%self.scale) def forward(self, x, mask=None, dummy=False): B, C, H, W = x.shape fused_x = self.proj_in(x) pwa, abc = paddle.split(fused_x, (self.dims[0], sum(self.dims)), axis=1) #第一个分离部分 dw_abc = self.dwconv(abc) * self.scale dw_list = paddle.split(dw_abc, self.dims, axis=1) #将特征分成对应的几个部分，也就是第二个split x = pwa * dw_list[0] for i in range(self.order -1): x = self.pws[i](x) * dw_list[i+1] #这里就是逐元素相乘操作 x = self.proj_out(x) return x
其余部分和ConNeXt类似，这里就不进行详细的展示
5. ImageNet实验结果
下面我们对模型在ImageNet验证集上效果进行展示。
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#解压数据集!mkdir data/ILSVRC2012!tar -xf ~/data/data105740/ILSVRC2012_val.tar -C ~/data/ILSVRC2012
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#导入必要的库import osimport cv2import numpy as npimport warningsimport paddleimport paddle.vision.transforms as Tfrom PIL import Imagewarnings.filterwarnings('ignore')# 构建数据集class ILSVRC2012(paddle.io.Dataset): def __init__(self, root, label_list, transform, backend='pil'): self.transform = transform self.root = root self.label_list = label_list self.backend = backend self.load_datas() def load_datas(self): self.imgs = [] self.labels = [] with open(self.label_list, 'r') as f: for line in f: img, label = line[:-1].split(' ') self.imgs.append(os.path.join(self.root, img)) self.labels.append(int(label)) def __getitem__(self, idx): label = self.labels[idx] image = self.imgs[idx] if self.backend=='cv2': image = cv2.imread(image) else: image = Image.open(image).convert('RGB') image = self.transform(image) return image.astype('float32'), np.array(label).astype('int64') def __len__(self): return len(self.imgs)val_transforms = T.Compose([ T.Resize(int(224 / 0.96), interpolation='bicubic'), T.CenterCrop(224), T.ToTensor(), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])val_transform_384 = T.Compose([ T.Resize(int(384 / 0.96), interpolation='bicubic'), T.CenterCrop(384), T.ToTensor(), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
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%cd /home/aistudio/
/home/aistudio
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#创建模型，这里我们复现三个不同体量模型from Hornet import hornet_tiny_7x7,hornet_small_7x7,hornet_base_gfmodel = hornet_tiny_7x7(drop_path_rate = 0.,layer_scale_init_value = 1e-6)model_small = hornet_small_7x7(drop_path_rate = 0.,layer_scale_init_value = 1e-6)model_base_gf = hornet_base_gf(drop_path_rate = 0.,layer_scale_init_value = 1e-6)
W0801 16:01:38.262773 286 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1W0801 16:01:38.267696 286 gpu_resources.cc:91] device: 0, cuDNN Version: 7.6.
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#导入权重model.load_dict(paddle.load('/home/aistudio/hornet_tiny_7x7.pdparams'))model = paddle.Model(model)model_small.load_dict(paddle.load('/home/aistudio/hornet_small_7x7.pdparams'))model_small = paddle.Model(model_small)model_base_gf.load_dict(paddle.load("/home/aistudio/hornet_base_gf.pdparams"))model_base_gf = paddle.Model(model_base_gf)
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#模型结构model.summary((1, 3, 224, 224))
--------------------------------------------------------------------------- Layer (type) Input Shape Output Shape Param # =========================================================================== Conv2D-1 [[1, 3, 224, 224]] [1, 64, 56, 56] 3,136 LayerNorm-1 [[1, 64, 56, 56]] [1, 64, 56, 56] 128 LayerNorm-5 [[1, 64, 56, 56]] [1, 64, 56, 56] 128 Conv2D-5 [[1, 64, 56, 56]] [1, 128, 56, 56] 8,320 Conv2D-6 [[1, 96, 56, 56]] [1, 96, 56, 56] 4,800 Conv2D-8 [[1, 32, 56, 56]] [1, 64, 56, 56] 2,112 Conv2D-7 [[1, 64, 56, 56]] [1, 64, 56, 56] 4,160 gnconv-1 [[1, 64, 56, 56]] [1, 64, 56, 56] 0 Identity-1 [[1, 64, 56, 56]] [1, 64, 56, 56] 0 LayerNorm-6 [[1, 56, 56, 64]] [1, 56, 56, 64] 128 Linear-1 [[1, 56, 56, 64]] [1, 56, 56, 256] 16,640 GELU-1 [[1, 56, 56, 256]] [1, 56, 56, 256] 0 Linear-2 [[1, 56, 56, 256]] [1, 56, 56, 64] 16,448 Block-1 [[1, 64, 56, 56]] [1, 64, 56, 56] 128 LayerNorm-7 [[1, 64, 56, 56]] [1, 64, 56, 56] 128 Conv2D-9 [[1, 64, 56, 56]] [1, 128, 56, 56] 8,320 Conv2D-10 [[1, 96, 56, 56]] [1, 96, 56, 56] 4,800 Conv2D-12 [[1, 32, 56, 56]] [1, 64, 56, 56] 2,112 Conv2D-11 [[1, 64, 56, 56]] [1, 64, 56, 56] 4,160 gnconv-2 [[1, 64, 56, 56]] [1, 64, 56, 56] 0 Identity-2 [[1, 64, 56, 56]] [1, 64, 56, 56] 0 LayerNorm-8 [[1, 56, 56, 64]] [1, 56, 56, 64] 128 Linear-3 [[1, 56, 56, 64]] [1, 56, 56, 256] 16,640 GELU-2 [[1, 56, 56, 256]] [1, 56, 56, 256] 0 Linear-4 [[1, 56, 56, 256]] [1, 56, 56, 64] 16,448 Block-2 [[1, 64, 56, 56]] [1, 64, 56, 56] 128 LayerNorm-2 [[1, 64, 56, 56]] [1, 64, 56, 56] 128 Conv2D-2 [[1, 64, 56, 56]] [1, 128, 28, 28] 32,896 LayerNorm-9 [[1, 128, 28, 28]] [1, 128, 28, 28] 256 Conv2D-13 [[1, 128, 28, 28]] [1, 256, 28, 28] 33,024 Conv2D-14 [[1, 224, 28, 28]] [1, 224, 28, 28] 11,200 Conv2D-16 [[1, 32, 28, 28]] [1, 64, 28, 28] 2,112 Conv2D-17 [[1, 64, 28, 28]] [1, 128, 28, 28] 8,320 Conv2D-15 [[1, 128, 28, 28]] [1, 128, 28, 28] 16,512 gnconv-3 [[1, 128, 28, 28]] [1, 128, 28, 28] 0 Identity-3 [[1, 128, 28, 28]] [1, 128, 28, 28] 0 LayerNorm-10 [[1, 28, 28, 128]] [1, 28, 28, 128] 256 Linear-5 [[1, 28, 28, 128]] [1, 28, 28, 512] 66,048 GELU-3 [[1, 28, 28, 512]] [1, 28, 28, 512] 0 Linear-6 [[1, 28, 28, 512]] [1, 28, 28, 128] 65,664 Block-3 [[1, 128, 28, 28]] [1, 128, 28, 28] 256 LayerNorm-11 [[1, 128, 28, 28]] [1, 128, 28, 28] 256 Conv2D-18 [[1, 128, 28, 28]] [1, 256, 28, 28] 33,024 Conv2D-19 [[1, 224, 28, 28]] [1, 224, 28, 28] 11,200 Conv2D-21 [[1, 32, 28, 28]] [1, 64, 28, 28] 2,112 Conv2D-22 [[1, 64, 28, 28]] [1, 128, 28, 28] 8,320 Conv2D-20 [[1, 128, 28, 28]] [1, 128, 28, 28] 16,512 gnconv-4 [[1, 128, 28, 28]] [1, 128, 28, 28] 0 Identity-4 [[1, 128, 28, 28]] [1, 128, 28, 28] 0 LayerNorm-12 [[1, 28, 28, 128]] [1, 28, 28, 128] 256 Linear-7 [[1, 28, 28, 128]] [1, 28, 28, 512] 66,048 GELU-4 [[1, 28, 28, 512]] [1, 28, 28, 512] 0 Linear-8 [[1, 28, 28, 512]] [1, 28, 28, 128] 65,664 Block-4 [[1, 128, 28, 28]] [1, 128, 28, 28] 256 LayerNorm-13 [[1, 128, 28, 28]] [1, 128, 28, 28] 256 Conv2D-23 [[1, 128, 28, 28]] [1, 256, 28, 28] 33,024 Conv2D-24 [[1, 224, 28, 28]] [1, 224, 28, 28] 11,200 Conv2D-26 [[1, 32, 28, 28]] [1, 64, 28, 28] 2,112 Conv2D-27 [[1, 64, 28, 28]] [1, 128, 28, 28] 8,320 Conv2D-25 [[1, 128, 28, 28]] [1, 128, 28, 28] 16,512 gnconv-5 [[1, 128, 28, 28]] [1, 128, 28, 28] 0 Identity-5 [[1, 128, 28, 28]] [1, 128, 28, 28] 0 LayerNorm-14 [[1, 28, 28, 128]] [1, 28, 28, 128] 256 Linear-9 [[1, 28, 28, 128]] [1, 28, 28, 512] 66,048 GELU-5 [[1, 28, 28, 512]] [1, 28, 28, 512] 0 Linear-10 [[1, 28, 28, 512]] [1, 28, 28, 128] 65,664 Block-5 [[1, 128, 28, 28]] [1, 128, 28, 28] 256 LayerNorm-3 [[1, 128, 28, 28]] [1, 128, 28, 28] 256 Conv2D-3 [[1, 128, 28, 28]] [1, 256, 14, 14] 131,328 LayerNorm-15 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-28 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-29 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-31 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-32 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-33 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-30 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-6 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-6 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-16 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-11 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-6 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-12 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-6 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-17 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-34 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-35 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-37 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-38 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-39 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-36 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-7 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-7 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-18 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-13 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-7 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-14 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-7 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-19 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-40 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-41 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-43 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-44 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-45 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-42 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-8 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-8 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-20 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-15 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-8 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-16 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-8 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-21 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-46 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-47 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-49 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-50 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-51 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-48 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-9 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-9 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-22 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-17 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-9 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-18 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-9 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-23 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-52 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-53 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-55 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-56 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-57 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-54 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-10 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-10 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-24 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-19 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-10 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-20 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-10 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-25 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-58 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-59 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-61 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-62 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-63 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-60 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-11 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-11 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-26 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-21 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-11 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-22 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-11 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-27 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-64 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-65 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-67 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-68 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-69 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-66 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-12 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-12 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-28 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-23 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-12 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-24 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-12 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-29 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-70 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-71 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-73 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-74 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-75 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-72 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-13 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-13 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-30 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-25 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-13 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-26 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-13 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-31 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-76 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-77 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-79 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-80 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-81 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-78 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-14 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-14 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-32 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-27 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-14 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-28 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-14 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-33 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-82 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-83 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-85 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-86 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-87 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-84 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-15 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-15 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-34 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-29 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-15 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-30 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-15 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-35 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-88 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-89 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-91 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-92 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-93 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-90 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-16 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-16 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-36 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-31 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-16 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-32 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-16 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-37 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-94 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-95 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-97 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-98 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-99 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-96 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-17 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-17 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-38 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-33 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-17 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-34 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-17 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-39 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-100 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-101 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-103 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-104 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-105 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-102 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-18 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-18 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-40 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-35 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-18 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-36 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-18 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-41 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-106 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-107 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-109 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-110 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-111 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-108 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-19 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-19 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-42 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-37 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-19 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-38 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-19 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-43 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-112 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-113 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-115 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-116 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-117 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-114 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-20 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-20 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-44 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-39 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-20 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-40 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-20 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-45 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-118 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-119 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-121 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-122 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-123 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-120 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-21 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-21 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-46 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-41 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-21 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-42 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-21 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-47 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-124 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-125 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-127 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-128 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-129 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-126 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-22 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-22 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-48 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-43 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-22 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-44 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-22 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-49 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-130 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,584 Conv2D-131 [[1, 480, 14, 14]] [1, 480, 14, 14] 24,000 Conv2D-133 [[1, 32, 14, 14]] [1, 64, 14, 14] 2,112 Conv2D-134 [[1, 64, 14, 14]] [1, 128, 14, 14] 8,320 Conv2D-135 [[1, 128, 14, 14]] [1, 256, 14, 14] 33,024 Conv2D-132 [[1, 256, 14, 14]] [1, 256, 14, 14] 65,792 gnconv-23 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 Identity-23 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 LayerNorm-50 [[1, 14, 14, 256]] [1, 14, 14, 256] 512 Linear-45 [[1, 14, 14, 256]] [1, 14, 14, 1024] 263,168 GELU-23 [[1, 14, 14, 1024]] [1, 14, 14, 1024] 0 Linear-46 [[1, 14, 14, 1024]] [1, 14, 14, 256] 262,400 Block-23 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 LayerNorm-4 [[1, 256, 14, 14]] [1, 256, 14, 14] 512 Conv2D-4 [[1, 256, 14, 14]] [1, 512, 7, 7] 524,800 LayerNorm-51 [[1, 512, 7, 7]] [1, 512, 7, 7] 1,024 Conv2D-136 [[1, 512, 7, 7]] [1, 1024, 7, 7] 525,312 Conv2D-137 [[1, 992, 7, 7]] [1, 992, 7, 7] 49,600 Conv2D-139 [[1, 32, 7, 7]] [1, 64, 7, 7] 2,112 Conv2D-140 [[1, 64, 7, 7]] [1, 128, 7, 7] 8,320 Conv2D-141 [[1, 128, 7, 7]] [1, 256, 7, 7] 33,024 Conv2D-142 [[1, 256, 7, 7]] [1, 512, 7, 7] 131,584 Conv2D-138 [[1, 512, 7, 7]] [1, 512, 7, 7] 262,656 gnconv-24 [[1, 512, 7, 7]] [1, 512, 7, 7] 0 Identity-24 [[1, 512, 7, 7]] [1, 512, 7, 7] 0 LayerNorm-52 [[1, 7, 7, 512]] [1, 7, 7, 512] 1,024 Linear-47 [[1, 7, 7, 512]] [1, 7, 7, 2048] 1,050,624 GELU-24 [[1, 7, 7, 2048]] [1, 7, 7, 2048] 0 Linear-48 [[1, 7, 7, 2048]] [1, 7, 7, 512] 1,049,088 Block-24 [[1, 512, 7, 7]] [1, 512, 7, 7] 1,024 LayerNorm-53 [[1, 512, 7, 7]] [1, 512, 7, 7] 1,024 Conv2D-143 [[1, 512, 7, 7]] [1, 1024, 7, 7] 525,312 Conv2D-144 [[1, 992, 7, 7]] [1, 992, 7, 7] 49,600 Conv2D-146 [[1, 32, 7, 7]] [1, 64, 7, 7] 2,112 Conv2D-147 [[1, 64, 7, 7]] [1, 128, 7, 7] 8,320 Conv2D-148 [[1, 128, 7, 7]] [1, 256, 7, 7] 33,024 Conv2D-149 [[1, 256, 7, 7]] [1, 512, 7, 7] 131,584 Conv2D-145 [[1, 512, 7, 7]] [1, 512, 7, 7] 262,656 gnconv-25 [[1, 512, 7, 7]] [1, 512, 7, 7] 0 Identity-25 [[1, 512, 7, 7]] [1, 512, 7, 7] 0 LayerNorm-54 [[1, 7, 7, 512]] [1, 7, 7, 512] 1,024 Linear-49 [[1, 7, 7, 512]] [1, 7, 7, 2048] 1,050,624 GELU-25 [[1, 7, 7, 2048]] [1, 7, 7, 2048] 0 Linear-50 [[1, 7, 7, 2048]] [1, 7, 7, 512] 1,049,088 Block-25 [[1, 512, 7, 7]] [1, 512, 7, 7] 1,024 LayerNorm-55 [[1, 512]] [1, 512] 1,024 Linear-51 [[1, 512]] [1, 1000] 513,000 ===========================================================================Total params: 22,409,512Trainable params: 22,409,512Non-trainable params: 0---------------------------------------------------------------------------Input size (MB): 0.57Forward/backward pass size (MB): 263.41Params size (MB): 85.49Estimated Total Size (MB): 349.47---------------------------------------------------------------------------
{'total_params': 22409512, 'trainable_params': 22409512}
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#tiny模型结果model.prepare(metrics=paddle.metric.Accuracy(topk=(1, 5)))val_dataset = ILSVRC2012('/home/aistudio/data/ILSVRC2012/ILSVRC2012_val', transform=val_transforms, label_list='/home/aistudio/data/ILSVRC2012/ILSVRC2012_val/val_list.txt', backend='pil')acc = model.evaluate(val_dataset, batch_size=32, num_workers=4, verbose=1)print(acc)
Eval begin...step 1563/1563 [==============================] - acc_top1: 0.8270 - acc_top5: 0.9637 - 98ms/step Eval samples: 50000{'acc_top1': 0.82698, 'acc_top5': 0.96374}
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#small模型结果model_small.prepare(metrics=paddle.metric.Accuracy(topk=(1, 5)))val_dataset = ILSVRC2012('/home/aistudio/data/ILSVRC2012/ILSVRC2012_val', transform=val_transforms, label_list='/home/aistudio/data/ILSVRC2012/ILSVRC2012_val/val_list.txt', backend='pil')acc = model_small.evaluate(val_dataset, batch_size=32, num_workers=4, verbose=1)print(acc)
Eval begin...step 1563/1563 [==============================] - acc_top1: 0.8390 - acc_top5: 0.9681 - 111ms/step Eval samples: 50000{'acc_top1': 0.83898, 'acc_top5': 0.96814}
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#base模型结果model_base_gf.prepare(metrics=paddle.metric.Accuracy(topk=(1, 5)))val_dataset = ILSVRC2012('/home/aistudio/data/ILSVRC2012/ILSVRC2012_val', transform=val_transforms, label_list='/home/aistudio/data/ILSVRC2012/ILSVRC2012_val/val_list.txt', backend='pil')acc = model_base_gf.evaluate(val_dataset, batch_size=32, num_workers=4, verbose=1)print(acc)
Eval begin...step 1563/1563 [==============================] - acc_top1: 0.8448 - acc_top5: 0.9701 - 183ms/step Eval samples: 50000{'acc_top1': 0.84476, 'acc_top5': 0.97008}
6、Flowers102数据集训练对比
为了公平比较CNN、Transformer与HorNet，本文借助Paddleclas在Flowers102数据集上对ResNet50vd、Swin、HorNet进行对比
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#解压数据集!mkdir data/flowers!tar -xf ~/data/data19852/flowers102.tar -C ~/data/flowers
[]
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%cd PaddleClas/
/home/aistudio/PaddleClas
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#安装必要库!pip install -r requirements.txt
首先进行HorNet的训练和验证
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!python -m paddle.distributed.launch --gpus 0 tools/train.py -c ppcls/configs/hornet_tiny_7x7.yaml -o Global.pretrained_model=/home/aistudio/hornet_tiny_7x7
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!python tools/eval.py -c ppcls/configs/hornet_tiny_7x7.yaml -o Global.pretrained_model=output/hornet_tiny_7x7/best_model
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import MutableMapping/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Iterable, Mapping/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Sized[2022/08/01 17:19:29] ppcls INFO: ============================================================= PaddleClas is powered by PaddlePaddle ! =============================================================== ==== For more info please go to the following website. ==== ==== https://github.com/PaddlePaddle/PaddleClas =============================================================[2022/08/01 17:19:29] ppcls INFO: Arch : [2022/08/01 17:19:29] ppcls INFO: class_num : 102[2022/08/01 17:19:29] ppcls INFO: drop_path_rate : 0.2[2022/08/01 17:19:29] ppcls INFO: head_init_scale : 1.0[2022/08/01 17:19:29] ppcls INFO: layer_scale_init_value : 1e-06[2022/08/01 17:19:29] ppcls INFO: name : hornet_tiny_7x7[2022/08/01 17:19:29] ppcls INFO: DataLoader : [2022/08/01 17:19:29] ppcls INFO: Eval : [2022/08/01 17:19:29] ppcls INFO: dataset : [2022/08/01 17:19:29] ppcls INFO: cls_label_path : /home/aistudio/data/flowers/oxford-102-flowers/oxford-102-flowers/valid.txt[2022/08/01 17:19:29] ppcls INFO: image_root : /home/aistudio/data/flowers/oxford-102-flowers/oxford-102-flowers[2022/08/01 17:19:29] ppcls INFO: name : ImageNetDataset[2022/08/01 17:19:29] ppcls INFO: transform_ops : [2022/08/01 17:19:29] ppcls INFO: DecodeImage : [2022/08/01 17:19:29] ppcls INFO: channel_first : False[2022/08/01 17:19:29] ppcls INFO: to_rgb : True[2022/08/01 17:19:29] ppcls INFO: ResizeImage : [2022/08/01 17:19:29] ppcls INFO: backend : pil[2022/08/01 17:19:29] ppcls INFO: interpolation : bicubic[2022/08/01 17:19:29] ppcls INFO: resize_short : 256[2022/08/01 17:19:29] ppcls INFO: CropImage : [2022/08/01 17:19:29] ppcls INFO: size : 224[2022/08/01 17:19:29] ppcls INFO: NormalizeImage : [2022/08/01 17:19:29] ppcls INFO: mean : [0.485, 0.456, 0.406][2022/08/01 17:19:29] ppcls INFO: order : [2022/08/01 17:19:29] ppcls INFO: scale : 1.0/255.0[2022/08/01 17:19:29] ppcls INFO: std : [0.229, 0.224, 0.225][2022/08/01 17:19:29] ppcls INFO: loader : [2022/08/01 17:19:29] ppcls INFO: num_workers : 4[2022/08/01 17:19:29] ppcls INFO: use_shared_memory : True[2022/08/01 17:19:29] ppcls INFO: sampler : [2022/08/01 17:19:29] ppcls INFO: batch_size : 128[2022/08/01 17:19:29] ppcls INFO: drop_last : False[2022/08/01 17:19:29] ppcls INFO: name : DistributedBatchSampler[2022/08/01 17:19:29] ppcls INFO: shuffle : False[2022/08/01 17:19:29] ppcls INFO: Train : [2022/08/01 17:19:29] ppcls INFO: dataset : [2022/08/01 17:19:29] ppcls INFO: batch_transform_ops : [2022/08/01 17:19:29] ppcls INFO: OpSampler : [2022/08/01 17:19:29] ppcls INFO: CutmixOperator : [2022/08/01 17:19:29] ppcls INFO: alpha : 1.0[2022/08/01 17:19:29] ppcls INFO: prob : 0.5[2022/08/01 17:19:29] ppcls INFO: MixupOperator : [2022/08/01 17:19:29] ppcls INFO: alpha : 0.8[2022/08/01 17:19:29] ppcls INFO: prob : 0.5[2022/08/01 17:19:29] ppcls INFO: cls_label_path : /home/aistudio/data/flowers/oxford-102-flowers/oxford-102-flowers/train.txt[2022/08/01 17:19:29] ppcls INFO: image_root : /home/aistudio/data/flowers/oxford-102-flowers/oxford-102-flowers[2022/08/01 17:19:29] ppcls INFO: name : ImageNetDataset[2022/08/01 17:19:29] ppcls INFO: transform_ops : [2022/08/01 17:19:29] ppcls INFO: DecodeImage : [2022/08/01 17:19:29] ppcls INFO: channel_first : False[2022/08/01 17:19:29] ppcls INFO: to_rgb : True[2022/08/01 17:19:29] ppcls INFO: RandCropImage : [2022/08/01 17:19:29] ppcls INFO: backend : pil[2022/08/01 17:19:29] ppcls INFO: interpolation : bicubic[2022/08/01 17:19:29] ppcls INFO: size : 224[2022/08/01 17:19:29] ppcls INFO: RandFlipImage : [2022/08/01 17:19:29] ppcls INFO: flip_code : 1[2022/08/01 17:19:29] ppcls INFO: TimmAutoAugment : [2022/08/01 17:19:29] ppcls INFO: config_str : rand-m9-mstd0.5-inc1[2022/08/01 17:19:29] ppcls INFO: img_size : 224[2022/08/01 17:19:29] ppcls INFO: interpolation : bicubic[2022/08/01 17:19:29] ppcls INFO: NormalizeImage : [2022/08/01 17:19:29] ppcls INFO: mean : [0.485, 0.456, 0.406][2022/08/01 17:19:29] ppcls INFO: order : [2022/08/01 17:19:29] ppcls INFO: scale : 1.0/255.0[2022/08/01 17:19:29] ppcls INFO: std : [0.229, 0.224, 0.225][2022/08/01 17:19:29] ppcls INFO: RandomErasing : [2022/08/01 17:19:29] ppcls INFO: EPSILON : 0.25[2022/08/01 17:19:29] ppcls INFO: ------------------------------------------------------------[2022/08/01 17:19:29] ppcls INFO: attempt : 10[2022/08/01 17:19:29] ppcls INFO: mode : pixel[2022/08/01 17:19:29] ppcls INFO: r1 : 0.3[2022/08/01 17:19:29] ppcls INFO: sh : 1.0/3.0[2022/08/01 17:19:29] ppcls INFO: sl : 0.02[2022/08/01 17:19:29] ppcls INFO: use_log_aspect : True[2022/08/01 17:19:29] ppcls INFO: loader : [2022/08/01 17:19:29] ppcls INFO: num_workers : 4[2022/08/01 17:19:29] ppcls INFO: use_shared_memory : True[2022/08/01 17:19:29] ppcls INFO: sampler : [2022/08/01 17:19:29] ppcls INFO: batch_size : 32[2022/08/01 17:19:29] ppcls INFO: drop_last : False[2022/08/01 17:19:29] ppcls INFO: name : DistributedBatchSampler[2022/08/01 17:19:29] ppcls INFO: shuffle : True[2022/08/01 17:19:29] ppcls INFO: EMA : [2022/08/01 17:19:29] ppcls INFO: decay : 0.9999[2022/08/01 17:19:29] ppcls INFO: ------------------------------------------------------------[2022/08/01 17:19:29] ppcls INFO: Global : [2022/08/01 17:19:29] ppcls INFO: checkpoints : None[2022/08/01 17:19:29] ppcls INFO: device : gpu[2022/08/01 17:19:29] ppcls INFO: epochs : 50[2022/08/01 17:19:29] ppcls INFO: eval_during_train : True[2022/08/01 17:19:29] ppcls INFO: eval_interval : 1[2022/08/01 17:19:29] ppcls INFO: image_shape : [3, 224, 224][2022/08/01 17:19:29] ppcls INFO: output_dir : ./output/[2022/08/01 17:19:29] ppcls INFO: pretrained_model : output/hornet_tiny_7x7/best_model[2022/08/01 17:19:29] ppcls INFO: print_batch_step : 10[2022/08/01 17:19:29] ppcls INFO: save_inference_dir : ./inference[2022/08/01 17:19:29] ppcls INFO: save_interval : 1[2022/08/01 17:19:29] ppcls INFO: update_freq : 4[2022/08/01 17:19:29] ppcls INFO: use_visualdl : False[2022/08/01 17:19:29] ppcls INFO: Infer : [2022/08/01 17:19:29] ppcls INFO: PostProcess : [2022/08/01 17:19:29] ppcls INFO: class_id_map_file : ./dataset/flowers102/flowers102_label_list.txt[2022/08/01 17:19:29] ppcls INFO: name : Topk[2022/08/01 17:19:29] ppcls INFO: topk : 5[2022/08/01 17:19:29] ppcls INFO: batch_size : 10[2022/08/01 17:19:29] ppcls INFO: infer_imgs : docs/images/inference_deployment/whl_demo.jpg[2022/08/01 17:19:29] ppcls INFO: transforms : [2022/08/01 17:19:29] ppcls INFO: DecodeImage : [2022/08/01 17:19:29] ppcls INFO: channel_first : False[2022/08/01 17:19:29] ppcls INFO: to_rgb : True[2022/08/01 17:19:29] ppcls INFO: ResizeImage : [2022/08/01 17:19:29] ppcls INFO: resize_short : 256[2022/08/01 17:19:29] ppcls INFO: CropImage : [2022/08/01 17:19:29] ppcls INFO: size : 224[2022/08/01 17:19:29] ppcls INFO: NormalizeImage : [2022/08/01 17:19:29] ppcls INFO: mean : [0.485, 0.456, 0.406][2022/08/01 17:19:29] ppcls INFO: order : [2022/08/01 17:19:29] ppcls INFO: scale : 1.0/255.0[2022/08/01 17:19:29] ppcls INFO: std : [0.229, 0.224, 0.225][2022/08/01 17:19:29] ppcls INFO: ToCHWImage : None[2022/08/01 17:19:29] ppcls INFO: Loss : [2022/08/01 17:19:29] ppcls INFO: Eval : [2022/08/01 17:19:29] ppcls INFO: CELoss : [2022/08/01 17:19:29] ppcls INFO: weight : 1.0[2022/08/01 17:19:29] ppcls INFO: Train : [2022/08/01 17:19:29] ppcls INFO: CELoss : [2022/08/01 17:19:29] ppcls INFO: weight : 1.0[2022/08/01 17:19:29] ppcls INFO: Metric : [2022/08/01 17:19:29] ppcls INFO: Eval : [2022/08/01 17:19:29] ppcls INFO: TopkAcc : [2022/08/01 17:19:29] ppcls INFO: topk : [1, 5][2022/08/01 17:19:29] ppcls INFO: Train : [2022/08/01 17:19:29] ppcls INFO: TopkAcc : [2022/08/01 17:19:29] ppcls INFO: topk : [1, 5][2022/08/01 17:19:29] ppcls INFO: Optimizer : [2022/08/01 17:19:29] ppcls INFO: beta1 : 0.9[2022/08/01 17:19:29] ppcls INFO: beta2 : 0.999[2022/08/01 17:19:29] ppcls INFO: epsilon : 1e-08[2022/08/01 17:19:29] ppcls INFO: lr : [2022/08/01 17:19:29] ppcls INFO: eta_min : 1e-06[2022/08/01 17:19:29] ppcls INFO: learning_rate : 0.0005[2022/08/01 17:19:29] ppcls INFO: name : Cosine[2022/08/01 17:19:29] ppcls INFO: warmup_epoch : 5[2022/08/01 17:19:29] ppcls INFO: warmup_start_lr : 0[2022/08/01 17:19:29] ppcls INFO: name : AdamW[2022/08/01 17:19:29] ppcls INFO: one_dim_param_no_weight_decay : True[2022/08/01 17:19:29] ppcls INFO: weight_decay : 0.05[2022/08/01 17:19:29] ppcls INFO: train with paddle 2.3.1 and device Place(gpu:0)W0801 17:19:29.716073 14503 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1W0801 17:19:29.721376 14503 gpu_resources.cc:91] device: 0, cuDNN Version: 7.6.+++++++[2022/08/01 17:19:36] ppcls INFO: [Eval][Epoch 0][Iter: 0/8]CELoss: 3.77862, loss: 3.77862, top1: 0.95312, top5: 0.99219, batch_cost: 5.59765s, reader_cost: 3.28722, ips: 22.86673 images/sec[2022/08/01 17:19:38] ppcls INFO: [Eval][Epoch 0][Avg]CELoss: 3.75967, loss: 3.75967, top1: 0.96961, top5: 0.99314
接着是ResNet50vd的训练和验证
In [ ]
!python -m paddle.distributed.launch --gpus 0 tools/train.py -c ppcls/configs/resnet50.yaml -o Global.pretrained_model=https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams
In [ ]
!python tools/eval.py -c ppcls/configs/resnet50.yaml -o Global.pretrained_model=output/ResNet50_vd/best_model
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import MutableMapping/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Iterable, Mapping/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Sized[2022/08/01 17:29:21] ppcls INFO: ============================================================= PaddleClas is powered by PaddlePaddle ! =============================================================== ==== For more info please go to the following website. ==== ==== https://github.com/PaddlePaddle/PaddleClas =============================================================[2022/08/01 17:29:21] ppcls INFO: Arch : [2022/08/01 17:29:21] ppcls INFO: class_num : 102[2022/08/01 17:29:21] ppcls INFO: name : ResNet50_vd[2022/08/01 17:29:21] ppcls INFO: DataLoader : [2022/08/01 17:29:21] ppcls INFO: Eval : [2022/08/01 17:29:21] ppcls INFO: dataset : [2022/08/01 17:29:21] ppcls INFO: cls_label_path : /home/aistudio/data/flowers/oxford-102-flowers/oxford-102-flowers/valid.txt[2022/08/01 17:29:21] ppcls INFO: image_root : /home/aistudio/data/flowers/oxford-102-flowers/oxford-102-flowers[2022/08/01 17:29:21] ppcls INFO: name : ImageNetDataset[2022/08/01 17:29:21] ppcls INFO: transform_ops : [2022/08/01 17:29:21] ppcls INFO: DecodeImage : [2022/08/01 17:29:21] ppcls INFO: channel_first : False[2022/08/01 17:29:21] ppcls INFO: to_rgb : True[2022/08/01 17:29:21] ppcls INFO: ResizeImage : [2022/08/01 17:29:21] ppcls INFO: resize_short : 256[2022/08/01 17:29:21] ppcls INFO: CropImage : [2022/08/01 17:29:21] ppcls INFO: size : 224[2022/08/01 17:29:21] ppcls INFO: NormalizeImage : [2022/08/01 17:29:21] ppcls INFO: mean : [0.485, 0.456, 0.406][2022/08/01 17:29:21] ppcls INFO: order : [2022/08/01 17:29:21] ppcls INFO: scale : 1.0/255.0[2022/08/01 17:29:21] ppcls INFO: std : [0.229, 0.224, 0.225][2022/08/01 17:29:21] ppcls INFO: loader : [2022/08/01 17:29:21] ppcls INFO: num_workers : 4[2022/08/01 17:29:21] ppcls INFO: use_shared_memory : True[2022/08/01 17:29:21] ppcls INFO: sampler : [2022/08/01 17:29:21] ppcls INFO: batch_size : 128[2022/08/01 17:29:21] ppcls INFO: drop_last : False[2022/08/01 17:29:21] ppcls INFO: name : DistributedBatchSampler[2022/08/01 17:29:21] ppcls INFO: shuffle : False[2022/08/01 17:29:21] ppcls INFO: Train : [2022/08/01 17:29:21] ppcls INFO: dataset : [2022/08/01 17:29:21] ppcls INFO: cls_label_path : /home/aistudio/data/flowers/oxford-102-flowers/oxford-102-flowers/train.txt[2022/08/01 17:29:21] ppcls INFO: image_root : /home/aistudio/data/flowers/oxford-102-flowers/oxford-102-flowers[2022/08/01 17:29:21] ppcls INFO: name : ImageNetDataset[2022/08/01 17:29:21] ppcls INFO: transform_ops : [2022/08/01 17:29:21] ppcls INFO: DecodeImage : [2022/08/01 17:29:21] ppcls INFO: channel_first : False[2022/08/01 17:29:21] ppcls INFO: to_rgb : True[2022/08/01 17:29:21] ppcls INFO: RandCropImage : [2022/08/01 17:29:21] ppcls INFO: size : 224[2022/08/01 17:29:21] ppcls INFO: RandFlipImage : [2022/08/01 17:29:21] ppcls INFO: flip_code : 1[2022/08/01 17:29:21] ppcls INFO: NormalizeImage : [2022/08/01 17:29:21] ppcls INFO: mean : [0.485, 0.456, 0.406][2022/08/01 17:29:21] ppcls INFO: order : [2022/08/01 17:29:21] ppcls INFO: scale : 1.0/255.0[2022/08/01 17:29:21] ppcls INFO: std : [0.229, 0.224, 0.225][2022/08/01 17:29:21] ppcls INFO: loader : [2022/08/01 17:29:21] ppcls INFO: num_workers : 4[2022/08/01 17:29:21] ppcls INFO: use_shared_memory : True[2022/08/01 17:29:21] ppcls INFO: sampler : [2022/08/01 17:29:21] ppcls INFO: batch_size : 32[2022/08/01 17:29:21] ppcls INFO: drop_last : False[2022/08/01 17:29:21] ppcls INFO: name : DistributedBatchSampler[2022/08/01 17:29:21] ppcls INFO: shuffle : True[2022/08/01 17:29:21] ppcls INFO: Global : [2022/08/01 17:29:21] ppcls INFO: checkpoints : None[2022/08/01 17:29:21] ppcls INFO: device : gpu[2022/08/01 17:29:21] ppcls INFO: epochs : 50[2022/08/01 17:29:21] ppcls INFO: eval_during_train : True[2022/08/01 17:29:21] ppcls INFO: eval_interval : 1[2022/08/01 17:29:21] ppcls INFO: image_shape : [3, 224, 224][2022/08/01 17:29:21] ppcls INFO: output_dir : ./output/[2022/08/01 17:29:21] ppcls INFO: pretrained_model : output/ResNet50_vd/best_model[2022/08/01 17:29:21] ppcls INFO: print_batch_step : 10[2022/08/01 17:29:21] ppcls INFO: save_inference_dir : ./inference[2022/08/01 17:29:21] ppcls INFO: save_interval : 1[2022/08/01 17:29:21] ppcls INFO: use_visualdl : False[2022/08/01 17:29:21] ppcls INFO: Infer : [2022/08/01 17:29:21] ppcls INFO: PostProcess : [2022/08/01 17:29:21] ppcls INFO: class_id_map_file : ./dataset/flowers102/flowers102_label_list.txt[2022/08/01 17:29:21] ppcls INFO: name : Topk[2022/08/01 17:29:21] ppcls INFO: topk : 5[2022/08/01 17:29:21] ppcls INFO: batch_size : 10[2022/08/01 17:29:21] ppcls INFO: infer_imgs : docs/images/inference_deployment/whl_demo.jpg[2022/08/01 17:29:21] ppcls INFO: transforms : [2022/08/01 17:29:21] ppcls INFO: DecodeImage : [2022/08/01 17:29:21] ppcls INFO: channel_first : False[2022/08/01 17:29:21] ppcls INFO: to_rgb : True[2022/08/01 17:29:21] ppcls INFO: ResizeImage : [2022/08/01 17:29:21] ppcls INFO: resize_short : 256[2022/08/01 17:29:21] ppcls INFO: CropImage : [2022/08/01 17:29:21] ppcls INFO: size : 224[2022/08/01 17:29:21] ppcls INFO: NormalizeImage : [2022/08/01 17:29:21] ppcls INFO: mean : [0.485, 0.456, 0.406][2022/08/01 17:29:21] ppcls INFO: order : [2022/08/01 17:29:21] ppcls INFO: scale : 1.0/255.0[2022/08/01 17:29:21] ppcls INFO: std : [0.229, 0.224, 0.225][2022/08/01 17:29:21] ppcls INFO: ToCHWImage : None[2022/08/01 17:29:21] ppcls INFO: Loss : [2022/08/01 17:29:21] ppcls INFO: Eval : [2022/08/01 17:29:21] ppcls INFO: CELoss : [2022/08/01 17:29:21] ppcls INFO: weight : 1.0[2022/08/01 17:29:21] ppcls INFO: Train : [2022/08/01 17:29:21] ppcls INFO: CELoss : [2022/08/01 17:29:21] ppcls INFO: weight : 1.0[2022/08/01 17:29:21] ppcls INFO: Metric : [2022/08/01 17:29:21] ppcls INFO: Eval : [2022/08/01 17:29:21] ppcls INFO: TopkAcc : [2022/08/01 17:29:21] ppcls INFO: topk : [1, 5][2022/08/01 17:29:21] ppcls INFO: Train : [2022/08/01 17:29:21] ppcls INFO: TopkAcc : [2022/08/01 17:29:21] ppcls INFO: topk : [1, 5][2022/08/01 17:29:21] ppcls INFO: Optimizer : [2022/08/01 17:29:21] ppcls INFO: lr : [2022/08/01 17:29:21] ppcls INFO: learning_rate : 0.0125[2022/08/01 17:29:21] ppcls INFO: name : Cosine[2022/08/01 17:29:21] ppcls INFO: warmup_epoch : 5[2022/08/01 17:29:21] ppcls INFO: momentum : 0.9[2022/08/01 17:29:21] ppcls INFO: name : Momentum[2022/08/01 17:29:21] ppcls INFO: regularizer : [2022/08/01 17:29:21] ppcls INFO: coeff : 1e-05[2022/08/01 17:29:21] ppcls INFO: name : L2[2022/08/01 17:29:21] ppcls INFO: train with paddle 2.3.1 and device Place(gpu:0)W0801 17:29:21.801292 22024 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1W0801 17:29:21.806051 22024 gpu_resources.cc:91] device: 0, cuDNN Version: 7.6.+++++++[2022/08/01 17:29:26] ppcls INFO: [Eval][Epoch 0][Iter: 0/8]CELoss: 0.32419, loss: 0.32419, top1: 0.92188, top5: 0.96875, batch_cost: 3.66914s, reader_cost: 1.63267, ips: 34.88556 images/sec[2022/08/01 17:29:27] ppcls INFO: [Eval][Epoch 0][Avg]CELoss: 0.21094, loss: 0.21094, top1: 0.95588, top5: 0.98529
最后是Swin的验证
In [37]
!python -m paddle.distributed.launch --gpus 0 tools/train.py -c ppcls/configs/swin.yaml -o Global.pretrained_model=https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams
In [38]
!python tools/eval.py -c ppcls/configs/swin.yaml -o Global.pretrained_model=output/SwinTransformer_tiny_patch4_window7_224/best_model
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import MutableMapping/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Iterable, Mapping/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Sized[2022/08/01 17:51:34] ppcls INFO: ============================================================= PaddleClas is powered by PaddlePaddle ! =============================================================== ==== For more info please go to the following website. ==== ==== https://github.com/PaddlePaddle/PaddleClas =============================================================[2022/08/01 17:51:34] ppcls INFO: Arch : [2022/08/01 17:51:34] ppcls INFO: class_num : 102[2022/08/01 17:51:34] ppcls INFO: name : SwinTransformer_tiny_patch4_window7_224[2022/08/01 17:51:34] ppcls INFO: DataLoader : [2022/08/01 17:51:34] ppcls INFO: Eval : [2022/08/01 17:51:34] ppcls INFO: dataset : [2022/08/01 17:51:34] ppcls INFO: cls_label_path : /home/aistudio/data/flowers/oxford-102-flowers/oxford-102-flowers/valid.txt[2022/08/01 17:51:34] ppcls INFO: image_root : /home/aistudio/data/flowers/oxford-102-flowers/oxford-102-flowers[2022/08/01 17:51:34] ppcls INFO: name : ImageNetDataset[2022/08/01 17:51:34] ppcls INFO: transform_ops : [2022/08/01 17:51:34] ppcls INFO: DecodeImage : [2022/08/01 17:51:34] ppcls INFO: channel_first : False[2022/08/01 17:51:34] ppcls INFO: to_rgb : True[2022/08/01 17:51:34] ppcls INFO: ResizeImage : [2022/08/01 17:51:34] ppcls INFO: backend : pil[2022/08/01 17:51:34] ppcls INFO: interpolation : bicubic[2022/08/01 17:51:34] ppcls INFO: resize_short : 256[2022/08/01 17:51:34] ppcls INFO: CropImage : [2022/08/01 17:51:34] ppcls INFO: size : 224[2022/08/01 17:51:34] ppcls INFO: NormalizeImage : [2022/08/01 17:51:34] ppcls INFO: mean : [0.485, 0.456, 0.406][2022/08/01 17:51:34] ppcls INFO: order : [2022/08/01 17:51:34] ppcls INFO: scale : 1.0/255.0[2022/08/01 17:51:34] ppcls INFO: std : [0.229, 0.224, 0.225][2022/08/01 17:51:34] ppcls INFO: loader : [2022/08/01 17:51:34] ppcls INFO: num_workers : 4[2022/08/01 17:51:34] ppcls INFO: use_shared_memory : True[2022/08/01 17:51:34] ppcls INFO: sampler : [2022/08/01 17:51:34] ppcls INFO: batch_size : 128[2022/08/01 17:51:34] ppcls INFO: drop_last : False[2022/08/01 17:51:34] ppcls INFO: name : DistributedBatchSampler[2022/08/01 17:51:34] ppcls INFO: shuffle : False[2022/08/01 17:51:34] ppcls INFO: Train : [2022/08/01 17:51:34] ppcls INFO: dataset : [2022/08/01 17:51:34] ppcls INFO: batch_transform_ops : [2022/08/01 17:51:34] ppcls INFO: OpSampler : [2022/08/01 17:51:34] ppcls INFO: CutmixOperator : [2022/08/01 17:51:34] ppcls INFO: alpha : 1.0[2022/08/01 17:51:34] ppcls INFO: prob : 0.5[2022/08/01 17:51:34] ppcls INFO: MixupOperator : [2022/08/01 17:51:34] ppcls INFO: alpha : 0.8[2022/08/01 17:51:34] ppcls INFO: prob : 0.5[2022/08/01 17:51:34] ppcls INFO: cls_label_path : /home/aistudio/data/flowers/oxford-102-flowers/oxford-102-flowers/train.txt[2022/08/01 17:51:34] ppcls INFO: image_root : /home/aistudio/data/flowers/oxford-102-flowers/oxford-102-flowers[2022/08/01 17:51:34] ppcls INFO: name : ImageNetDataset[2022/08/01 17:51:34] ppcls INFO: transform_ops : [2022/08/01 17:51:34] ppcls INFO: DecodeImage : [2022/08/01 17:51:34] ppcls INFO: channel_first : False[2022/08/01 17:51:34] ppcls INFO: to_rgb : True[2022/08/01 17:51:34] ppcls INFO: RandCropImage : [2022/08/01 17:51:34] ppcls INFO: backend : pil[2022/08/01 17:51:34] ppcls INFO: interpolation : bicubic[2022/08/01 17:51:34] ppcls INFO: size : 224[2022/08/01 17:51:34] ppcls INFO: RandFlipImage : [2022/08/01 17:51:34] ppcls INFO: flip_code : 1[2022/08/01 17:51:34] ppcls INFO: TimmAutoAugment : [2022/08/01 17:51:34] ppcls INFO: config_str : rand-m9-mstd0.5-inc1[2022/08/01 17:51:34] ppcls INFO: img_size : 224[2022/08/01 17:51:34] ppcls INFO: interpolation : bicubic[2022/08/01 17:51:34] ppcls INFO: NormalizeImage : [2022/08/01 17:51:34] ppcls INFO: mean : [0.485, 0.456, 0.406][2022/08/01 17:51:34] ppcls INFO: order : [2022/08/01 17:51:34] ppcls INFO: scale : 1.0/255.0[2022/08/01 17:51:34] ppcls INFO: std : [0.229, 0.224, 0.225][2022/08/01 17:51:34] ppcls INFO: RandomErasing : [2022/08/01 17:51:34] ppcls INFO: EPSILON : 0.25[2022/08/01 17:51:34] ppcls INFO: ------------------------------------------------------------[2022/08/01 17:51:34] ppcls INFO: attempt : 10[2022/08/01 17:51:34] ppcls INFO: mode : pixel[2022/08/01 17:51:34] ppcls INFO: r1 : 0.3[2022/08/01 17:51:34] ppcls INFO: sh : 1.0/3.0[2022/08/01 17:51:34] ppcls INFO: sl : 0.02[2022/08/01 17:51:34] ppcls INFO: use_log_aspect : True[2022/08/01 17:51:34] ppcls INFO: loader : [2022/08/01 17:51:34] ppcls INFO: num_workers : 4[2022/08/01 17:51:34] ppcls INFO: use_shared_memory : True[2022/08/01 17:51:34] ppcls INFO: sampler : [2022/08/01 17:51:34] ppcls INFO: batch_size : 32[2022/08/01 17:51:34] ppcls INFO: drop_last : False[2022/08/01 17:51:34] ppcls INFO: name : DistributedBatchSampler[2022/08/01 17:51:34] ppcls INFO: shuffle : True[2022/08/01 17:51:34] ppcls INFO: Global : [2022/08/01 17:51:34] ppcls INFO: checkpoints : None[2022/08/01 17:51:34] ppcls INFO: device : gpu[2022/08/01 17:51:34] ppcls INFO: epochs : 50[2022/08/01 17:51:34] ppcls INFO: eval_during_train : True[2022/08/01 17:51:34] ppcls INFO: eval_interval : 1[2022/08/01 17:51:34] ppcls INFO: image_shape : [3, 224, 224][2022/08/01 17:51:34] ppcls INFO: output_dir : ./output/[2022/08/01 17:51:34] ppcls INFO: pretrained_model : output/SwinTransformer_tiny_patch4_window7_224/best_model[2022/08/01 17:51:34] ppcls INFO: print_batch_step : 10[2022/08/01 17:51:34] ppcls INFO: save_inference_dir : ./inference[2022/08/01 17:51:34] ppcls INFO: save_interval : 1[2022/08/01 17:51:34] ppcls INFO: to_static : False[2022/08/01 17:51:34] ppcls INFO: use_visualdl : False[2022/08/01 17:51:34] ppcls INFO: Infer : [2022/08/01 17:51:34] ppcls INFO: PostProcess : [2022/08/01 17:51:34] ppcls INFO: class_id_map_file : ppcls/utils/imagenet1k_label_list.txt[2022/08/01 17:51:34] ppcls INFO: name : Topk[2022/08/01 17:51:34] ppcls INFO: topk : 5[2022/08/01 17:51:34] ppcls INFO: batch_size : 10[2022/08/01 17:51:34] ppcls INFO: infer_imgs : docs/images/inference_deployment/whl_demo.jpg[2022/08/01 17:51:34] ppcls INFO: transforms : [2022/08/01 17:51:34] ppcls INFO: DecodeImage : [2022/08/01 17:51:34] ppcls INFO: channel_first : False[2022/08/01 17:51:34] ppcls INFO: to_rgb : True[2022/08/01 17:51:34] ppcls INFO: ResizeImage : [2022/08/01 17:51:34] ppcls INFO: backend : pil[2022/08/01 17:51:34] ppcls INFO: interpolation : bicubic[2022/08/01 17:51:34] ppcls INFO: resize_short : 256[2022/08/01 17:51:34] ppcls INFO: CropImage : [2022/08/01 17:51:34] ppcls INFO: size : 224[2022/08/01 17:51:34] ppcls INFO: NormalizeImage : [2022/08/01 17:51:34] ppcls INFO: mean : [0.485, 0.456, 0.406][2022/08/01 17:51:34] ppcls INFO: order : [2022/08/01 17:51:34] ppcls INFO: scale : 1.0/255.0[2022/08/01 17:51:34] ppcls INFO: std : [0.229, 0.224, 0.225][2022/08/01 17:51:34] ppcls INFO: ToCHWImage : None[2022/08/01 17:51:34] ppcls INFO: Loss : [2022/08/01 17:51:34] ppcls INFO: Eval : [2022/08/01 17:51:34] ppcls INFO: CELoss : [2022/08/01 17:51:34] ppcls INFO: weight : 1.0[2022/08/01 17:51:34] ppcls INFO: Train : [2022/08/01 17:51:34] ppcls INFO: CELoss : [2022/08/01 17:51:34] ppcls INFO: epsilon : 0.1[2022/08/01 17:51:34] ppcls INFO: weight : 1.0[2022/08/01 17:51:34] ppcls INFO: Metric : [2022/08/01 17:51:34] ppcls INFO: Eval : [2022/08/01 17:51:34] ppcls INFO: TopkAcc : [2022/08/01 17:51:34] ppcls INFO: topk : [1, 5][2022/08/01 17:51:34] ppcls INFO: Optimizer : [2022/08/01 17:51:34] ppcls INFO: beta1 : 0.9[2022/08/01 17:51:34] ppcls INFO: beta2 : 0.999[2022/08/01 17:51:34] ppcls INFO: epsilon : 1e-08[2022/08/01 17:51:34] ppcls INFO: lr : [2022/08/01 17:51:34] ppcls INFO: eta_min : 2e-05[2022/08/01 17:51:34] ppcls INFO: learning_rate : 1.25e-05[2022/08/01 17:51:34] ppcls INFO: name : Cosine[2022/08/01 17:51:34] ppcls INFO: warmup_epoch : 5[2022/08/01 17:51:34] ppcls INFO: warmup_start_lr : 2e-06[2022/08/01 17:51:34] ppcls INFO: name : AdamW[2022/08/01 17:51:34] ppcls INFO: no_weight_decay_name : absolute_pos_embed relative_position_bias_table .bias norm[2022/08/01 17:51:34] ppcls INFO: one_dim_param_no_weight_decay : True[2022/08/01 17:51:34] ppcls INFO: weight_decay : 0.05[2022/08/01 17:51:34] ppcls INFO: train with paddle 2.3.1 and device Place(gpu:0)W0801 17:51:34.209131 26352 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1W0801 17:51:34.214239 26352 gpu_resources.cc:91] device: 0, cuDNN Version: 7.6.+++++++[2022/08/01 17:51:41] ppcls INFO: [Eval][Epoch 0][Iter: 0/8]CELoss: 0.49065, loss: 0.49065, top1: 0.89844, top5: 0.97656, batch_cost: 5.42404s, reader_cost: 2.71487, ips: 23.59865 images/sec[2022/08/01 17:51:42] ppcls INFO: [Eval][Epoch 0][Avg]CELoss: 0.39882, loss: 0.39882, top1: 0.94510, top5: 0.98529
对比实验结果
ResNet50vd0.95588Swin-Transformer0.94510HorNet0.96961
从实验结果可以看出，HorNet优势还是很明显的，这也表明HorNet中模块的有效性
以上就是HorNet: 高效的空间交互模块的详细内容，更多请关注创想鸟其它相关文章！
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