本文复现了EPSANet，其核心是高效金字塔注意力分割模块（EPSA）。该模块通过SPC获取多尺度特征图，经SE提取注意力、Softmax校准后加权输出。文中给出模块及网络完整代码，构建了EPSANet50等模型，并在Cifar10上与ResNet-50对比，显示其性能更优，泛化性强，适用于多种下游任务。
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Hi guy！我们这下又见面了，这次是来复现 EPSANet
关于注意力机制，实际上最近我比较关注enhance一类的文章，这篇论文也可以算是划分到enhance一类，先上性能图
不得不说这个图做的还怪好看，可视化效果也蛮直观，性能还可以
不仅是图像分类，而且在下游任务比如目标检测分割上，也展现了良好的性能
EPSA模块可以有效地提取更细粒度的多尺度空间信息，同时可以建立更长距离的通道依赖关系
其中 SPC module 如下
PSA模块主要分四个步骤实现：
输出得到一个多尺度特征信息注意力加权之后的特征图。该特征图多尺度信息表示能力更丰富。
In [1]
import paddleimport paddle.nn as nnimport mathdef conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1, groups=1):
"""standard convolution with padding"""
return nn.Conv2D(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation, groups=groups, bias_attr=False)def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2D(in_planes, out_planes, kernel_size=1, stride=stride, bias_attr=False)class SEWeightModule(nn.Layer):
def __init__(self, channels, reduction=16):
super(SEWeightModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2D(1)
self.fc1 = nn.Conv2D(channels, channels//reduction, kernel_size=1, padding=0)
self.relu = nn.ReLU()
self.fc2 = nn.Conv2D(channels//reduction, channels, kernel_size=1, padding=0)
self.sigmoid = nn.Sigmoid() def forward(self, x):
out = self.avg_pool(x)
out = self.fc1(out)
out = self.relu(out)
out = self.fc2(out)
weight = self.sigmoid(out) return weightclass PSAModule(nn.Layer):
def __init__(self, inplans, planes, conv_kernels=[3, 5, 7, 9], stride=1, conv_groups=[1, 4, 8, 16]):
super(PSAModule, self).__init__()
self.conv_1 = conv(inplans, planes//4, kernel_size=conv_kernels[0], padding=conv_kernels[0]//2,
stride=stride, groups=conv_groups[0])
self.conv_2 = conv(inplans, planes//4, kernel_size=conv_kernels[1], padding=conv_kernels[1]//2,
stride=stride, groups=conv_groups[1])
self.conv_3 = conv(inplans, planes//4, kernel_size=conv_kernels[2], padding=conv_kernels[2]//2,
stride=stride, groups=conv_groups[2])
self.conv_4 = conv(inplans, planes//4, kernel_size=conv_kernels[3], padding=conv_kernels[3]//2,
stride=stride, groups=conv_groups[3])
self.se = SEWeightModule(planes // 4)
self.split_channel = planes // 4
self.softmax = nn.Softmax(axis=1) def forward(self, x):
# stage 1
batch_size = x.shape[0]
x1 = self.conv_1(x)
x2 = self.conv_2(x)
x3 = self.conv_3(x)
x4 = self.conv_4(x)
feats = paddle.concat((x1, x2, x3, x4), axis=1)
feats = feats.reshape([batch_size, 4, self.split_channel, feats.shape[2], feats.shape[3]])
# stage 2
x1_se = self.se(x1)
x2_se = self.se(x2)
x3_se = self.se(x3)
x4_se = self.se(x4)
x_se = paddle.concat((x1_se, x2_se, x3_se, x4_se), axis=1)
attention_vectors = x_se.reshape([batch_size, 4, self.split_channel, 1, 1])
attention_vectors = self.softmax(attention_vectors) # stage 3
# stage 4
feats_weight = feats * attention_vectors for i in range(4):
x_se_weight_fp = feats_weight[:, i, :, :] if i == 0:
out = x_se_weight_fp else:
out = paddle.concat((x_se_weight_fp, out), axis=1) return out/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations def convert_to_list(value, n, name, dtype=np.int):In [3]
# 验证 PSA psa = PSAModule(64, 64) x = paddle.randn([32, 64, 112, 112]) y = psa(x)print(y)
现在搭建一个EPSANet，它基于EPSA Block，从下图可以看出EPSA Block用PSA模块代替了ResNet Block的Conv 3x3卷积
与ResNet-50对比
class EPSABlock(nn.Layer):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, norm_layer=None, conv_kernels=[3, 5, 7, 9],
conv_groups=[1, 4, 8, 16]):
super(EPSABlock, self).__init__() if norm_layer is None:
norm_layer = nn.BatchNorm2D
self.conv1 = conv1x1(inplanes, planes)
self.bn1 = norm_layer(planes)
self.conv2 = PSAModule(planes, planes, stride=stride, conv_kernels=conv_kernels, conv_groups=conv_groups)
self.bn2 = norm_layer(planes)
self.conv3 = conv1x1(planes, planes * self.expansion)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU()
self.downsample = downsample
self.stride = stride def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out) if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out) return outclass EPSANet(nn.Layer):
def __init__(self, block, layers, num_classes=1000):
super(EPSANet, self).__init__()
self.inplanes = 64
self.conv1 = nn.Conv2D(3, 64, kernel_size=7, stride=2, padding=3, bias_attr=False)
self.bn1 = nn.BatchNorm2D(64)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layers(block, 64, layers[0], stride=1)
self.layer2 = self._make_layers(block, 128, layers[1], stride=2)
self.layer3 = self._make_layers(block, 256, layers[2], stride=2)
self.layer4 = self._make_layers(block, 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2D((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.sublayers(): if isinstance(m, nn.Conv2D):
n = m.weight.shape[2] * m.weight.shape[3] * m.weight.shape[0]
normal_ = nn.initializer.Normal(std=math.sqrt(2. / n))
normal_(m.weight) def _make_layers(self, block, planes, num_blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2D(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias_attr=False),
nn.BatchNorm2D(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion for i in range(1, num_blocks):
layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.reshape([x.shape[0], -1])
x = self.fc(x) return x
def epsanet50(**kwarg):
model = EPSANet(EPSABlock, [3, 4, 6, 3], **kwarg) return modeldef epsanet101():
model = EPSANet(EPSABlock, [3, 4, 23, 3], **kwarg) return model
paddle.Model(epsanet50()).summary((1,3,224,224))
--------------------------------------------------------------------------------
Layer (type) Input Shape Output Shape Param #
================================================================================
Conv2D-13 [[1, 3, 224, 224]] [1, 64, 112, 112] 9,408
BatchNorm2D-1 [[1, 64, 112, 112]] [1, 64, 112, 112] 256
ReLU-3 [[1, 64, 112, 112]] [1, 64, 112, 112] 0
MaxPool2D-1 [[1, 64, 112, 112]] [1, 64, 56, 56] 0
Conv2D-15 [[1, 64, 56, 56]] [1, 64, 56, 56] 4,096
BatchNorm2D-3 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
ReLU-5 [[1, 256, 56, 56]] [1, 256, 56, 56] 0
Conv2D-16 [[1, 64, 56, 56]] [1, 16, 56, 56] 9,216
Conv2D-17 [[1, 64, 56, 56]] [1, 16, 56, 56] 6,400
Conv2D-18 [[1, 64, 56, 56]] [1, 16, 56, 56] 6,272
Conv2D-19 [[1, 64, 56, 56]] [1, 16, 56, 56] 5,184
AdaptiveAvgPool2D-3 [[1, 16, 56, 56]] [1, 16, 1, 1] 0
Conv2D-20 [[1, 16, 1, 1]] [1, 1, 1, 1] 17
ReLU-4 [[1, 1, 1, 1]] [1, 1, 1, 1] 0
Conv2D-21 [[1, 1, 1, 1]] [1, 16, 1, 1] 32
Sigmoid-3 [[1, 16, 1, 1]] [1, 16, 1, 1] 0
SEWeightModule-3 [[1, 16, 56, 56]] [1, 16, 1, 1] 0
Softmax-3 [[1, 4, 16, 1, 1]] [1, 4, 16, 1, 1] 0
PSAModule-3 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
BatchNorm2D-4 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
Conv2D-22 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384
BatchNorm2D-5 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024
Conv2D-14 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384
BatchNorm2D-2 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024
EPSABlock-1 [[1, 64, 56, 56]] [1, 256, 56, 56] 0
Conv2D-23 [[1, 256, 56, 56]] [1, 64, 56, 56] 16,384
BatchNorm2D-6 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
ReLU-7 [[1, 256, 56, 56]] [1, 256, 56, 56] 0
Conv2D-24 [[1, 64, 56, 56]] [1, 16, 56, 56] 9,216
Conv2D-25 [[1, 64, 56, 56]] [1, 16, 56, 56] 6,400
Conv2D-26 [[1, 64, 56, 56]] [1, 16, 56, 56] 6,272
Conv2D-27 [[1, 64, 56, 56]] [1, 16, 56, 56] 5,184
AdaptiveAvgPool2D-4 [[1, 16, 56, 56]] [1, 16, 1, 1] 0
Conv2D-28 [[1, 16, 1, 1]] [1, 1, 1, 1] 17
ReLU-6 [[1, 1, 1, 1]] [1, 1, 1, 1] 0
Conv2D-29 [[1, 1, 1, 1]] [1, 16, 1, 1] 32
Sigmoid-4 [[1, 16, 1, 1]] [1, 16, 1, 1] 0
SEWeightModule-4 [[1, 16, 56, 56]] [1, 16, 1, 1] 0
Softmax-4 [[1, 4, 16, 1, 1]] [1, 4, 16, 1, 1] 0
PSAModule-4 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
BatchNorm2D-7 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
Conv2D-30 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384
BatchNorm2D-8 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024
EPSABlock-2 [[1, 256, 56, 56]] [1, 256, 56, 56] 0
Conv2D-31 [[1, 256, 56, 56]] [1, 64, 56, 56] 16,384
BatchNorm2D-9 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
ReLU-9 [[1, 256, 56, 56]] [1, 256, 56, 56] 0
Conv2D-32 [[1, 64, 56, 56]] [1, 16, 56, 56] 9,216
Conv2D-33 [[1, 64, 56, 56]] [1, 16, 56, 56] 6,400
Conv2D-34 [[1, 64, 56, 56]] [1, 16, 56, 56] 6,272
Conv2D-35 [[1, 64, 56, 56]] [1, 16, 56, 56] 5,184
AdaptiveAvgPool2D-5 [[1, 16, 56, 56]] [1, 16, 1, 1] 0
Conv2D-36 [[1, 16, 1, 1]] [1, 1, 1, 1] 17
ReLU-8 [[1, 1, 1, 1]] [1, 1, 1, 1] 0
Conv2D-37 [[1, 1, 1, 1]] [1, 16, 1, 1] 32
Sigmoid-5 [[1, 16, 1, 1]] [1, 16, 1, 1] 0
SEWeightModule-5 [[1, 16, 56, 56]] [1, 16, 1, 1] 0
Softmax-5 [[1, 4, 16, 1, 1]] [1, 4, 16, 1, 1] 0
PSAModule-5 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
BatchNorm2D-10 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
Conv2D-38 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384
BatchNorm2D-11 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024
EPSABlock-3 [[1, 256, 56, 56]] [1, 256, 56, 56] 0
Conv2D-40 [[1, 256, 56, 56]] [1, 128, 56, 56] 32,768
BatchNorm2D-13 [[1, 128, 56, 56]] [1, 128, 56, 56] 512
ReLU-11 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
Conv2D-41 [[1, 128, 56, 56]] [1, 32, 28, 28] 36,864
Conv2D-42 [[1, 128, 56, 56]] [1, 32, 28, 28] 25,600
Conv2D-43 [[1, 128, 56, 56]] [1, 32, 28, 28] 25,088
Conv2D-44 [[1, 128, 56, 56]] [1, 32, 28, 28] 20,736
AdaptiveAvgPool2D-6 [[1, 32, 28, 28]] [1, 32, 1, 1] 0
Conv2D-45 [[1, 32, 1, 1]] [1, 2, 1, 1] 66
ReLU-10 [[1, 2, 1, 1]] [1, 2, 1, 1] 0
Conv2D-46 [[1, 2, 1, 1]] [1, 32, 1, 1] 96
Sigmoid-6 [[1, 32, 1, 1]] [1, 32, 1, 1] 0
SEWeightModule-6 [[1, 32, 28, 28]] [1, 32, 1, 1] 0
Softmax-6 [[1, 4, 32, 1, 1]] [1, 4, 32, 1, 1] 0
PSAModule-6 [[1, 128, 56, 56]] [1, 128, 28, 28] 0
BatchNorm2D-14 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
Conv2D-47 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536
BatchNorm2D-15 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
Conv2D-39 [[1, 256, 56, 56]] [1, 512, 28, 28] 131,072
BatchNorm2D-12 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
EPSABlock-4 [[1, 256, 56, 56]] [1, 512, 28, 28] 0
Conv2D-48 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536
BatchNorm2D-16 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-13 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
Conv2D-49 [[1, 128, 28, 28]] [1, 32, 28, 28] 36,864
Conv2D-50 [[1, 128, 28, 28]] [1, 32, 28, 28] 25,600
Conv2D-51 [[1, 128, 28, 28]] [1, 32, 28, 28] 25,088
Conv2D-52 [[1, 128, 28, 28]] [1, 32, 28, 28] 20,736
AdaptiveAvgPool2D-7 [[1, 32, 28, 28]] [1, 32, 1, 1] 0
Conv2D-53 [[1, 32, 1, 1]] [1, 2, 1, 1] 66
ReLU-12 [[1, 2, 1, 1]] [1, 2, 1, 1] 0
Conv2D-54 [[1, 2, 1, 1]] [1, 32, 1, 1] 96
Sigmoid-7 [[1, 32, 1, 1]] [1, 32, 1, 1] 0
SEWeightModule-7 [[1, 32, 28, 28]] [1, 32, 1, 1] 0
Softmax-7 [[1, 4, 32, 1, 1]] [1, 4, 32, 1, 1] 0
PSAModule-7 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
BatchNorm2D-17 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
Conv2D-55 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536
BatchNorm2D-18 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
EPSABlock-5 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
Conv2D-56 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536
BatchNorm2D-19 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-15 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
Conv2D-57 [[1, 128, 28, 28]] [1, 32, 28, 28] 36,864
Conv2D-58 [[1, 128, 28, 28]] [1, 32, 28, 28] 25,600
Conv2D-59 [[1, 128, 28, 28]] [1, 32, 28, 28] 25,088
Conv2D-60 [[1, 128, 28, 28]] [1, 32, 28, 28] 20,736
AdaptiveAvgPool2D-8 [[1, 32, 28, 28]] [1, 32, 1, 1] 0
Conv2D-61 [[1, 32, 1, 1]] [1, 2, 1, 1] 66
ReLU-14 [[1, 2, 1, 1]] [1, 2, 1, 1] 0
Conv2D-62 [[1, 2, 1, 1]] [1, 32, 1, 1] 96
Sigmoid-8 [[1, 32, 1, 1]] [1, 32, 1, 1] 0
SEWeightModule-8 [[1, 32, 28, 28]] [1, 32, 1, 1] 0
Softmax-8 [[1, 4, 32, 1, 1]] [1, 4, 32, 1, 1] 0
PSAModule-8 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
BatchNorm2D-20 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
Conv2D-63 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536
BatchNorm2D-21 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
EPSABlock-6 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
Conv2D-64 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536
BatchNorm2D-22 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-17 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
Conv2D-65 [[1, 128, 28, 28]] [1, 32, 28, 28] 36,864
Conv2D-66 [[1, 128, 28, 28]] [1, 32, 28, 28] 25,600
Conv2D-67 [[1, 128, 28, 28]] [1, 32, 28, 28] 25,088
Conv2D-68 [[1, 128, 28, 28]] [1, 32, 28, 28] 20,736
AdaptiveAvgPool2D-9 [[1, 32, 28, 28]] [1, 32, 1, 1] 0
Conv2D-69 [[1, 32, 1, 1]] [1, 2, 1, 1] 66
ReLU-16 [[1, 2, 1, 1]] [1, 2, 1, 1] 0
Conv2D-70 [[1, 2, 1, 1]] [1, 32, 1, 1] 96
Sigmoid-9 [[1, 32, 1, 1]] [1, 32, 1, 1] 0
SEWeightModule-9 [[1, 32, 28, 28]] [1, 32, 1, 1] 0
Softmax-9 [[1, 4, 32, 1, 1]] [1, 4, 32, 1, 1] 0
PSAModule-9 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
BatchNorm2D-23 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
Conv2D-71 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536
BatchNorm2D-24 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
EPSABlock-7 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
Conv2D-73 [[1, 512, 28, 28]] [1, 256, 28, 28] 131,072
BatchNorm2D-26 [[1, 256, 28, 28]] [1, 256, 28, 28] 1,024
ReLU-19 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-74 [[1, 256, 28, 28]] [1, 64, 14, 14] 147,456
Conv2D-75 [[1, 256, 28, 28]] [1, 64, 14, 14] 102,400
Conv2D-76 [[1, 256, 28, 28]] [1, 64, 14, 14] 100,352
Conv2D-77 [[1, 256, 28, 28]] [1, 64, 14, 14] 82,944
AdaptiveAvgPool2D-10 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Conv2D-78 [[1, 64, 1, 1]] [1, 4, 1, 1] 260
ReLU-18 [[1, 4, 1, 1]] [1, 4, 1, 1] 0
Conv2D-79 [[1, 4, 1, 1]] [1, 64, 1, 1] 320
Sigmoid-10 [[1, 64, 1, 1]] [1, 64, 1, 1] 0
SEWeightModule-10 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Softmax-10 [[1, 4, 64, 1, 1]] [1, 4, 64, 1, 1] 0
PSAModule-10 [[1, 256, 28, 28]] [1, 256, 14, 14] 0
BatchNorm2D-27 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
Conv2D-80 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-28 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
Conv2D-72 [[1, 512, 28, 28]] [1, 1024, 14, 14] 524,288
BatchNorm2D-25 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
EPSABlock-8 [[1, 512, 28, 28]] [1, 1024, 14, 14] 0
Conv2D-81 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144
BatchNorm2D-29 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-21 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-82 [[1, 256, 14, 14]] [1, 64, 14, 14] 147,456
Conv2D-83 [[1, 256, 14, 14]] [1, 64, 14, 14] 102,400
Conv2D-84 [[1, 256, 14, 14]] [1, 64, 14, 14] 100,352
Conv2D-85 [[1, 256, 14, 14]] [1, 64, 14, 14] 82,944
AdaptiveAvgPool2D-11 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Conv2D-86 [[1, 64, 1, 1]] [1, 4, 1, 1] 260
ReLU-20 [[1, 4, 1, 1]] [1, 4, 1, 1] 0
Conv2D-87 [[1, 4, 1, 1]] [1, 64, 1, 1] 320
Sigmoid-11 [[1, 64, 1, 1]] [1, 64, 1, 1] 0
SEWeightModule-11 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Softmax-11 [[1, 4, 64, 1, 1]] [1, 4, 64, 1, 1] 0
PSAModule-11 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
BatchNorm2D-30 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
Conv2D-88 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-31 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
EPSABlock-9 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-89 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144
BatchNorm2D-32 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-23 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-90 [[1, 256, 14, 14]] [1, 64, 14, 14] 147,456
Conv2D-91 [[1, 256, 14, 14]] [1, 64, 14, 14] 102,400
Conv2D-92 [[1, 256, 14, 14]] [1, 64, 14, 14] 100,352
Conv2D-93 [[1, 256, 14, 14]] [1, 64, 14, 14] 82,944
AdaptiveAvgPool2D-12 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Conv2D-94 [[1, 64, 1, 1]] [1, 4, 1, 1] 260
ReLU-22 [[1, 4, 1, 1]] [1, 4, 1, 1] 0
Conv2D-95 [[1, 4, 1, 1]] [1, 64, 1, 1] 320
Sigmoid-12 [[1, 64, 1, 1]] [1, 64, 1, 1] 0
SEWeightModule-12 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Softmax-12 [[1, 4, 64, 1, 1]] [1, 4, 64, 1, 1] 0
PSAModule-12 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
BatchNorm2D-33 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
Conv2D-96 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-34 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
EPSABlock-10 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-97 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144
BatchNorm2D-35 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-25 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-98 [[1, 256, 14, 14]] [1, 64, 14, 14] 147,456
Conv2D-99 [[1, 256, 14, 14]] [1, 64, 14, 14] 102,400
Conv2D-100 [[1, 256, 14, 14]] [1, 64, 14, 14] 100,352
Conv2D-101 [[1, 256, 14, 14]] [1, 64, 14, 14] 82,944
AdaptiveAvgPool2D-13 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Conv2D-102 [[1, 64, 1, 1]] [1, 4, 1, 1] 260
ReLU-24 [[1, 4, 1, 1]] [1, 4, 1, 1] 0
Conv2D-103 [[1, 4, 1, 1]] [1, 64, 1, 1] 320
Sigmoid-13 [[1, 64, 1, 1]] [1, 64, 1, 1] 0
SEWeightModule-13 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Softmax-13 [[1, 4, 64, 1, 1]] [1, 4, 64, 1, 1] 0
PSAModule-13 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
BatchNorm2D-36 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
Conv2D-104 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-37 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
EPSABlock-11 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-105 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144
BatchNorm2D-38 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-27 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-106 [[1, 256, 14, 14]] [1, 64, 14, 14] 147,456
Conv2D-107 [[1, 256, 14, 14]] [1, 64, 14, 14] 102,400
Conv2D-108 [[1, 256, 14, 14]] [1, 64, 14, 14] 100,352
Conv2D-109 [[1, 256, 14, 14]] [1, 64, 14, 14] 82,944
AdaptiveAvgPool2D-14 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Conv2D-110 [[1, 64, 1, 1]] [1, 4, 1, 1] 260
ReLU-26 [[1, 4, 1, 1]] [1, 4, 1, 1] 0
Conv2D-111 [[1, 4, 1, 1]] [1, 64, 1, 1] 320
Sigmoid-14 [[1, 64, 1, 1]] [1, 64, 1, 1] 0
SEWeightModule-14 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Softmax-14 [[1, 4, 64, 1, 1]] [1, 4, 64, 1, 1] 0
PSAModule-14 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
BatchNorm2D-39 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
Conv2D-112 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-40 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
EPSABlock-12 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-113 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144
BatchNorm2D-41 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-29 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-114 [[1, 256, 14, 14]] [1, 64, 14, 14] 147,456
Conv2D-115 [[1, 256, 14, 14]] [1, 64, 14, 14] 102,400
Conv2D-116 [[1, 256, 14, 14]] [1, 64, 14, 14] 100,352
Conv2D-117 [[1, 256, 14, 14]] [1, 64, 14, 14] 82,944
AdaptiveAvgPool2D-15 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Conv2D-118 [[1, 64, 1, 1]] [1, 4, 1, 1] 260
ReLU-28 [[1, 4, 1, 1]] [1, 4, 1, 1] 0
Conv2D-119 [[1, 4, 1, 1]] [1, 64, 1, 1] 320
Sigmoid-15 [[1, 64, 1, 1]] [1, 64, 1, 1] 0
SEWeightModule-15 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Softmax-15 [[1, 4, 64, 1, 1]] [1, 4, 64, 1, 1] 0
PSAModule-15 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
BatchNorm2D-42 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
Conv2D-120 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-43 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
EPSABlock-13 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-122 [[1, 1024, 14, 14]] [1, 512, 14, 14] 524,288
BatchNorm2D-45 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048
ReLU-31 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
Conv2D-123 [[1, 512, 14, 14]] [1, 128, 7, 7] 589,824
Conv2D-124124 [[1, 512, 14, 14]] [1, 128, 7,
7] 409,600
Conv2D-125 [[1, 512, 14, 14]] [1, 128, 7, 7] 401,408
Conv2D-126 [[1, 512, 14, 14]] [1, 128, 7, 7] 331,776
AdaptiveAvgPool2D-16 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Conv2D-127 [[1, 128, 1, 1]] [1, 8, 1, 1] 1,032
ReLU-30 [[1, 8, 1, 1]] [1, 8, 1, 1] 0
Conv2D-128 [[1, 8, 1, 1]] [1, 128, 1, 1] 1,152
Sigmoid-16 [[1, 128, 1, 1]] [1, 128, 1, 1] 0
SEWeightModule-16 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Softmax-16 [[1, 4, 128, 1, 1]] [1, 4, 128, 1, 1] 0
PSAModule-16 [[1, 512, 14, 14]] [1, 512, 7, 7] 0
BatchNorm2D-46 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
Conv2D-129 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576
BatchNorm2D-47 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
Conv2D-121 [[1, 1024, 14, 14]] [1, 2048, 7, 7] 2,097,152
BatchNorm2D-44 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
EPSABlock-14 [[1, 1024, 14, 14]] [1, 2048, 7, 7] 0
Conv2D-130 [[1, 2048, 7, 7]] [1, 512, 7, 7] 1,048,576
BatchNorm2D-48 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
ReLU-33 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
Conv2D-131 [[1, 512, 7, 7]] [1, 128, 7, 7] 589,824
Conv2D-132 [[1, 512, 7, 7]] [1, 128, 7, 7] 409,600
Conv2D-133 [[1, 512, 7, 7]] [1, 128, 7, 7] 401,408
Conv2D-134 [[1, 512, 7, 7]] [1, 128, 7, 7] 331,776
AdaptiveAvgPool2D-17 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Conv2D-135 [[1, 128, 1, 1]] [1, 8, 1, 1] 1,032
ReLU-32 [[1, 8, 1, 1]] [1, 8, 1, 1] 0
Conv2D-136 [[1, 8, 1, 1]] [1, 128, 1, 1] 1,152
Sigmoid-17 [[1, 128, 1, 1]] [1, 128, 1, 1] 0
SEWeightModule-17 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Softmax-17 [[1, 4, 128, 1, 1]] [1, 4, 128, 1, 1] 0
PSAModule-17 [[1, 512, 7, 7]] [1, 512, 7, 7] 0
BatchNorm2D-49 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
Conv2D-137 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576
BatchNorm2D-50 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
EPSABlock-15 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
Conv2D-138 [[1, 2048, 7, 7]] [1, 512, 7, 7] 1,048,576
BatchNorm2D-51 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
ReLU-35 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
Conv2D-139 [[1, 512, 7, 7]] [1, 128, 7, 7] 589,824
Conv2D-140 [[1, 512, 7, 7]] [1, 128, 7, 7] 409,600
Conv2D-141 [[1, 512, 7, 7]] [1, 128, 7, 7] 401,408
Conv2D-142 [[1, 512, 7, 7]] [1, 128, 7, 7] 331,776
AdaptiveAvgPool2D-18 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Conv2D-143 [[1, 128, 1, 1]] [1, 8, 1, 1] 1,032
ReLU-34 [[1, 8, 1, 1]] [1, 8, 1, 1] 0
Conv2D-144 [[1, 8, 1, 1]] [1, 128, 1, 1] 1,152
Sigmoid-18 [[1, 128, 1, 1]] [1, 128, 1, 1] 0
SEWeightModule-18 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Softmax-18 [[1, 4, 128, 1, 1]] [1, 4, 128, 1, 1] 0
PSAModule-18 [[1, 512, 7, 7]] [1, 512, 7, 7] 0
BatchNorm2D-52 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
Conv2D-145 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576
BatchNorm2D-53 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
EPSABlock-16 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
AdaptiveAvgPool2D-19 [[1, 2048, 7, 7]] [1, 2048, 1, 1] 0
Linear-1 [[1, 2048]] [1, 1000] 2,049,000
================================================================================
Total params: 22,614,835
Trainable params: 22,508,595
Non-trainable params: 106,240
--------------------------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 272.07
Params size (MB): 86.27
Estimated Total Size (MB): 358.91
--------------------------------------------------------------------------------
7] 409,600
Conv2D-125 [[1, 512, 14, 14]] [1, 128, 7, 7] 401,408
Conv2D-126 [[1, 512, 14, 14]] [1, 128, 7, 7] 331,776
AdaptiveAvgPool2D-16 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Conv2D-127 [[1, 128, 1, 1]] [1, 8, 1, 1] 1,032
ReLU-30 [[1, 8, 1, 1]] [1, 8, 1, 1] 0
Conv2D-128 [[1, 8, 1, 1]] [1, 128, 1, 1] 1,152
Sigmoid-16 [[1, 128, 1, 1]] [1, 128, 1, 1] 0
SEWeightModule-16 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Softmax-16 [[1, 4, 128, 1, 1]] [1, 4, 128, 1, 1] 0
PSAModule-16 [[1, 512, 14, 14]] [1, 512, 7, 7] 0
BatchNorm2D-46 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
Conv2D-129 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576
BatchNorm2D-47 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
Conv2D-121 [[1, 1024, 14, 14]] [1, 2048, 7, 7] 2,097,152
BatchNorm2D-44 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
EPSABlock-14 [[1, 1024, 14, 14]] [1, 2048, 7, 7] 0
Conv2D-130 [[1, 2048, 7, 7]] [1, 512, 7, 7] 1,048,576
BatchNorm2D-48 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
ReLU-33 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
Conv2D-131 [[1, 512, 7, 7]] [1, 128, 7, 7] 589,824
Conv2D-132 [[1, 512, 7, 7]] [1, 128, 7, 7] 409,600
Conv2D-133 [[1, 512, 7, 7]] [1, 128, 7, 7] 401,408
Conv2D-134 [[1, 512, 7, 7]] [1, 128, 7, 7] 331,776
AdaptiveAvgPool2D-17 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Conv2D-135 [[1, 128, 1, 1]] [1, 8, 1, 1] 1,032
ReLU-32 [[1, 8, 1, 1]] [1, 8, 1, 1] 0
Conv2D-136 [[1, 8, 1, 1]] [1, 128, 1, 1] 1,152
Sigmoid-17 [[1, 128, 1, 1]] [1, 128, 1, 1] 0
SEWeightModule-17 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Softmax-17 [[1, 4, 128, 1, 1]] [1, 4, 128, 1, 1] 0
PSAModule-17 [[1, 512, 7, 7]] [1, 512, 7, 7] 0
BatchNorm2D-49 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
Conv2D-137 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576
BatchNorm2D-50 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
EPSABlock-15 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
Conv2D-138 [[1, 2048, 7, 7]] [1, 512, 7, 7] 1,048,576
BatchNorm2D-51 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
ReLU-35 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
Conv2D-139 [[1, 512, 7, 7]] [1, 128, 7, 7] 589,824
Conv2D-140 [[1, 512, 7, 7]] [1, 128, 7, 7] 409,600
Conv2D-141 [[1, 512, 7, 7]] [1, 128, 7, 7] 401,408
Conv2D-142 [[1, 512, 7, 7]] [1, 128, 7, 7] 331,776
AdaptiveAvgPool2D-18 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Conv2D-143 [[1, 128, 1, 1]] [1, 8, 1, 1] 1,032
ReLU-34 [[1, 8, 1, 1]] [1, 8, 1, 1] 0
Conv2D-144 [[1, 8, 1, 1]] [1, 128, 1, 1] 1,152
Sigmoid-18 [[1, 128, 1, 1]] [1, 128, 1, 1] 0
SEWeightModule-18 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Softmax-18 [[1, 4, 128, 1, 1]] [1, 4, 128, 1, 1] 0
PSAModule-18 [[1, 512, 7, 7]] [1, 512, 7, 7] 0
BatchNorm2D-52 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
Conv2D-145 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576
BatchNorm2D-53 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
EPSABlock-16 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
AdaptiveAvgPool2D-19 [[1, 2048, 7, 7]] [1, 2048, 1, 1] 0
Linear-1 [[1, 2048]] [1, 1000] 2,049,000
================================================================================
Total params: 22,614,835
Trainable params: 22,508,595
Non-trainable params: 106,240
--------------------------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 272.07
Params size (MB): 86.27
Estimated Total Size (MB): 358.91
--------------------------------------------------------------------------------{'total_params': 22614835, 'trainable_params': 22508595}
如下图所示，我们对比EPSANet-50和Paddle官方ResNet-50，看看他们谁的性能更好
import paddle.vision.transforms as Tfrom paddle.vision.datasets import Cifar10
paddle.set_device('gpu')#数据准备transform = T.Compose([
T.Resize(size=(224,224)),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225],data_format='HWC'),
T.ToTensor()
])
train_dataset = Cifar10(mode='train', transform=transform)
val_dataset = Cifar10(mode='test', transform=transform)Cache file /home/aistudio/.cache/paddle/dataset/cifar/cifar-10-python.tar.gz not found, downloading https://dataset.bj.bcebos.com/cifar/cifar-10-python.tar.gz Begin to download Download finished
# ResNet-50 vs EPSANet-50resnet50=paddle.Model(paddle.vision.models.resnet50(num_classes=10)) epsanet50=paddle.Model(epsanet50(num_classes=10))
# ResNet-50resnet50.prepare(optimizer=paddle.optimizer.Adam(learning_rate=0.001, parameters=resnet50.parameters()),
loss=paddle.nn.CrossEntropyLoss(),
metrics=paddle.metric.Accuracy())
visualdl=paddle.callbacks.VisualDL(log_dir='resnet50_log') # 开启训练可视化resnet50.fit(
train_data=train_dataset,
eval_data=val_dataset,
batch_size=64,
epochs=10,
verbose=1,
callbacks=[visualdl]
)
In [10]
# EPSANet-50epsanet50.prepare(optimizer=paddle.optimizer.Adam(learning_rate=0.001, parameters=epsanet50.parameters()),
loss=paddle.nn.CrossEntropyLoss(),
metrics=paddle.metric.Accuracy())
visualdl=paddle.callbacks.VisualDL(log_dir='epsanet50_log') # 开启训练可视化epsanet50.fit(
train_data=train_dataset,
eval_data=val_dataset,
batch_size=64,
epochs=10,
verbose=1,
callbacks=[visualdl]
)
# python
# ai
# red
# 征信
# 浅谈
# 很好
# 还可以
# 最好的
# 适用于
# 并在
# 仅是
# 有效地
# 这下
# 可以看出
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