GoogLeNet卷积神经网络输出数据形参分析-笔记
分析结果为：
输入数据形状:[10, 3, 224, 224]
最后输出结果：linear_0 [10, 1] [1024, 1] [1]
子空间执行逻辑
def forward_old(self, x):
# 支路1只包含一个1x1卷积
p1 = F.relu(self.p1_1(x))
# 支路2包含 1x1卷积 + 3x3卷积
p2 = F.relu(self.p2_2(F.relu(self.p2_1(x))))
# 支路3包含 1x1卷积 + 5x5卷积
p3 = F.relu(self.p3_2(F.relu(self.p3_1(x))))
# 支路4包含 最大池化和1x1卷积
p4 = F.relu(self.p4_2(self.p4_1(x)))
# 将每个支路的输出特征图拼接在一起作为最终的输出结果
return paddle.concat([p1, p2, p3, p4], axis=1)
# return self.batchnorm()
其中拼接张量情况为：
拼接代码：paddle.concat([p1, p2, p3, p4], axis=1)
如
p1=[10,64,5,6,56]
p2=[10,128,56,56]
p3=[10,32,56,56]
p4=[10,32,56,56]
拼接后结果为：【10，256，56，56】 sum(64+128+32+32)=256
分析详细过程如下所示：文章来源：https://www.toymoban.com/news/detail-625706.html
PS E:\project\python> & D:/ProgramData/Anaconda3/python.exe e:/project/python/PM/GoogLeNet_PM.py
W0804 11:14:26.736726 5484 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 6.1, Driver API Version: 12.2, Runtime API Version: 10.2
W0804 11:14:26.747695 5484 gpu_resources.cc:91] device: 0, cuDNN Version: 7.6.
116
(10, 3, 224, 224)
[10, 3, 224, 224]
#第一模块 包括一个7X7的卷积
conv2d_0 [10, 64, 224, 224] [64, 3, 7, 7] [64]
# 3x3最大池化
max_pool2d_0 [10, 64, 112, 112]
# 第二个模块包含2个卷积层
conv2d_1 [10, 64, 112, 112] [64, 64, 1, 1] [64]
conv2d_2 [10, 192, 112, 112] [192, 64, 3, 3] [192]
# 3x3最大池化
max_pool2d_1 [10, 192, 56, 56]
# 第三个模块包含2个Inception块
print block3-1: Inception盗梦空间1 即子空间1
conv2d_3 [10, 64, 56, 56] [64, 192, 1, 1] [64]
conv2d_4 [10, 96, 56, 56] [96, 192, 1, 1] [96]
conv2d_5 [10, 128, 56, 56] [128, 96, 3, 3] [128]
conv2d_6 [10, 16, 56, 56] [16, 192, 1, 1] [16]
conv2d_7 [10, 32, 56, 56] [32, 16, 5, 5] [32]
max_pool2d_2 [10, 192, 56, 56]
conv2d_8 [10, 32, 56, 56] [32, 192, 1, 1] [32]
block3_1.shape= [10, 256, 56, 56]
print block3-2: Inception盗梦空间2 即子空间2
conv2d_9 [10, 128, 56, 56] [128, 256, 1, 1] [128]
conv2d_10 [10, 128, 56, 56] [128, 256, 1, 1] [128]
conv2d_11 [10, 192, 56, 56] [192, 128, 3, 3] [192]
conv2d_12 [10, 32, 56, 56] [32, 256, 1, 1] [32]
conv2d_13 [10, 96, 56, 56] [96, 32, 5, 5] [96]
max_pool2d_3 [10, 256, 56, 56]
conv2d_14 [10, 64, 56, 56] [64, 256, 1, 1] [64]
block3_2.shape= [10, 480, 56, 56]
# 3x3最大池化
max_pool2d_4 [10, 480, 28, 28]
# 第四个模块包含5个Inception块
print block4_1:
conv2d_15 [10, 192, 28, 28] [192, 480, 1, 1] [192]
conv2d_16 [10, 96, 28, 28] [96, 480, 1, 1] [96]
conv2d_17 [10, 208, 28, 28] [208, 96, 3, 3] [208]
conv2d_18 [10, 16, 28, 28] [16, 480, 1, 1] [16]
conv2d_19 [10, 48, 28, 28] [48, 16, 5, 5] [48]
max_pool2d_5 [10, 480, 28, 28]
conv2d_20 [10, 64, 28, 28] [64, 480, 1, 1] [64]
block4_1.shape= [10, 512, 28, 28]
print block4_2:
conv2d_21 [10, 160, 28, 28] [160, 512, 1, 1] [160]
conv2d_22 [10, 112, 28, 28] [112, 512, 1, 1] [112]
conv2d_23 [10, 224, 28, 28] [224, 112, 3, 3] [224]
conv2d_24 [10, 24, 28, 28] [24, 512, 1, 1] [24]
conv2d_25 [10, 64, 28, 28] [64, 24, 5, 5] [64]
max_pool2d_6 [10, 512, 28, 28]
conv2d_26 [10, 64, 28, 28] [64, 512, 1, 1] [64]
block4_2.shape= [10, 512, 28, 28]
print block4_3:
conv2d_27 [10, 128, 28, 28] [128, 512, 1, 1] [128]
conv2d_28 [10, 128, 28, 28] [128, 512, 1, 1] [128]
conv2d_29 [10, 256, 28, 28] [256, 128, 3, 3] [256]
conv2d_30 [10, 24, 28, 28] [24, 512, 1, 1] [24]
conv2d_31 [10, 64, 28, 28] [64, 24, 5, 5] [64]
max_pool2d_7 [10, 512, 28, 28]
conv2d_32 [10, 64, 28, 28] [64, 512, 1, 1] [64]
block4_3.shape= [10, 512, 28, 28]
print block4_4:
conv2d_33 [10, 112, 28, 28] [112, 512, 1, 1] [112]
conv2d_34 [10, 144, 28, 28] [144, 512, 1, 1] [144]
conv2d_35 [10, 288, 28, 28] [288, 144, 3, 3] [288]
conv2d_36 [10, 32, 28, 28] [32, 512, 1, 1] [32]
conv2d_37 [10, 64, 28, 28] [64, 32, 5, 5] [64]
max_pool2d_8 [10, 512, 28, 28]
conv2d_38 [10, 64, 28, 28] [64, 512, 1, 1] [64]
block4_4.shape= [10, 528, 28, 28]
print block4_5:
conv2d_39 [10, 256, 28, 28] [256, 528, 1, 1] [256]
conv2d_40 [10, 160, 28, 28] [160, 528, 1, 1] [160]
conv2d_41 [10, 320, 28, 28] [320, 160, 3, 3] [320]
conv2d_42 [10, 32, 28, 28] [32, 528, 1, 1] [32]
conv2d_43 [10, 128, 28, 28] [128, 32, 5, 5] [128]
max_pool2d_9 [10, 528, 28, 28]
conv2d_44 [10, 128, 28, 28] [128, 528, 1, 1] [128]
block4_5.shape= [10, 832, 28, 28]
max_pool2d_10 [10, 832, 14, 14]
# 第五个模块包含2个Inception块(block5_1,block5_2)
print block5_1:
conv2d_45 [10, 256, 14, 14] [256, 832, 1, 1] [256]
conv2d_46 [10, 160, 14, 14] [160, 832, 1, 1] [160]
conv2d_47 [10, 320, 14, 14] [320, 160, 3, 3] [320]
conv2d_48 [10, 32, 14, 14] [32, 832, 1, 1] [32]
conv2d_49 [10, 128, 14, 14] [128, 32, 5, 5] [128]
max_pool2d_11 [10, 832, 14, 14]
conv2d_50 [10, 128, 14, 14] [128, 832, 1, 1] [128]
block5_1.shape= [10, 832, 14, 14]
print block5_2:
conv2d_51 [10, 384, 14, 14] [384, 832, 1, 1] [384]
conv2d_52 [10, 192, 14, 14] [192, 832, 1, 1] [192]
conv2d_53 [10, 384, 14, 14] [384, 192, 3, 3] [384]
conv2d_54 [10, 48, 14, 14] [48, 832, 1, 1] [48]
conv2d_55 [10, 128, 14, 14] [128, 48, 5, 5] [128]
max_pool2d_12 [10, 832, 14, 14]
conv2d_56 [10, 128, 14, 14] [128, 832, 1, 1] [128]
block5_2.shape= [10, 1024, 14, 14]
adaptive_avg_pool2d_0 [10, 1024, 1, 1]
linear_0 [10, 1] [1024, 1] [1]
PS E:\project\python>
分析测试代码如下所示：文章来源地址https://www.toymoban.com/news/detail-625706.html
# GoogLeNet模型代码
#GoogLeNet卷积神经网络-笔记
import numpy as np
import paddle
from paddle.nn import Conv2D, MaxPool2D, AdaptiveAvgPool2D, Linear
## 组网
import paddle.nn.functional as F
#分析形参定制版本方法
def printItem(item,x):
# item是CNN类中的一个子层
# 查看经过子层之后的输出数据形状
try:
x = item(x)
except:
x = paddle.reshape(x, [x.shape[0], -1])
x = item(x)
if len(item.parameters())==2:
# 查看卷积和全连接层的数据和参数的形状，
# 其中item.parameters()[0]是权重参数w，item.parameters()[1]是偏置参数b
print(item.full_name(), x.shape, item.parameters()[0].shape, item.parameters()[1].shape)
else:
# 池化层没有参数
print(item.full_name(), x.shape)
return x;
# 定义Inception块
class Inception(paddle.nn.Layer):
def __init__(self, c0, c1, c2, c3, c4, **kwargs):
'''
Inception模块的实现代码，
c1,图(b)中第一条支路1x1卷积的输出通道数，数据类型是整数
c2,图(b)中第二条支路卷积的输出通道数，数据类型是tuple或list,
其中c2[0]是1x1卷积的输出通道数，c2[1]是3x3
c3,图(b)中第三条支路卷积的输出通道数，数据类型是tuple或list,
其中c3[0]是1x1卷积的输出通道数，c3[1]是3x3
c4,图(b)中第一条支路1x1卷积的输出通道数，数据类型是整数
'''
super(Inception, self).__init__()
# 依次创建Inception块每条支路上使用到的操作
self.p1_1 = Conv2D(in_channels=c0,out_channels=c1, kernel_size=1, stride=1)
self.p2_1 = Conv2D(in_channels=c0,out_channels=c2[0], kernel_size=1, stride=1)
self.p2_2 = Conv2D(in_channels=c2[0],out_channels=c2[1], kernel_size=3, padding=1, stride=1)
self.p3_1 = Conv2D(in_channels=c0,out_channels=c3[0], kernel_size=1, stride=1)
self.p3_2 = Conv2D(in_channels=c3[0],out_channels=c3[1], kernel_size=5, padding=2, stride=1)
self.p4_1 = MaxPool2D(kernel_size=3, stride=1, padding=1)
self.p4_2 = Conv2D(in_channels=c0,out_channels=c4, kernel_size=1, stride=1)
# # 新加一层batchnorm稳定收敛
# self.batchnorm = paddle.nn.BatchNorm2D(c1+c2[1]+c3[1]+c4)
def forward_old(self, x):
# 支路1只包含一个1x1卷积
p1 = F.relu(self.p1_1(x))
# 支路2包含 1x1卷积 + 3x3卷积
p2 = F.relu(self.p2_2(F.relu(self.p2_1(x))))
# 支路3包含 1x1卷积 + 5x5卷积
p3 = F.relu(self.p3_2(F.relu(self.p3_1(x))))
# 支路4包含 最大池化和1x1卷积
p4 = F.relu(self.p4_2(self.p4_1(x)))
# 将每个支路的输出特征图拼接在一起作为最终的输出结果
return paddle.concat([p1, p2, p3, p4], axis=1)
# return self.batchnorm()
#分析形参定制版本方法
def forward(self,x):
p1=printItem(self.p1_1,x)
p1=F.relu(p1)
#
p2=printItem(self.p2_1,x)
p2=F.relu(p2)
p2=printItem(self.p2_2,p2)
p2=F.relu(p2)
#
p3=printItem(self.p3_1,x)
p3=F.relu(p3)
p3=printItem(self.p3_2,p3)
p3=F.relu(p3)
#
p4=printItem(self.p4_1,x)
p4=printItem(self.p4_2,p4)
p4=F.relu(p4)
return paddle.concat([p1, p2, p3, p4], axis=1);
class GoogLeNet(paddle.nn.Layer):
def __init__(self):
super(GoogLeNet, self).__init__()
# GoogLeNet包含五个模块，每个模块后面紧跟一个池化层
# 第一个模块包含1个卷积层
self.conv1 = Conv2D(in_channels=3,out_channels=64, kernel_size=7, padding=3, stride=1)
# 3x3最大池化
self.pool1 = MaxPool2D(kernel_size=3, stride=2, padding=1)
# 第二个模块包含2个卷积层
self.conv2_1 = Conv2D(in_channels=64,out_channels=64, kernel_size=1, stride=1)
self.conv2_2 = Conv2D(in_channels=64,out_channels=192, kernel_size=3, padding=1, stride=1)
# 3x3最大池化
self.pool2 = MaxPool2D(kernel_size=3, stride=2, padding=1)
# 第三个模块包含2个Inception块
self.block3_1 = Inception(192, 64, (96, 128), (16, 32), 32)
self.block3_2 = Inception(256, 128, (128, 192), (32, 96), 64)
# 3x3最大池化
self.pool3 = MaxPool2D(kernel_size=3, stride=2, padding=1)
# 第四个模块包含5个Inception块
self.block4_1 = Inception(480, 192, (96, 208), (16, 48), 64)
self.block4_2 = Inception(512, 160, (112, 224), (24, 64), 64)
self.block4_3 = Inception(512, 128, (128, 256), (24, 64), 64)
self.block4_4 = Inception(512, 112, (144, 288), (32, 64), 64)
self.block4_5 = Inception(528, 256, (160, 320), (32, 128), 128)
# 3x3最大池化
self.pool4 = MaxPool2D(kernel_size=3, stride=2, padding=1)
# 第五个模块包含2个Inception块
self.block5_1 = Inception(832, 256, (160, 320), (32, 128), 128)
self.block5_2 = Inception(832, 384, (192, 384), (48, 128), 128)
# 全局池化，用的是global_pooling，不需要设置pool_stride
self.pool5 = AdaptiveAvgPool2D(output_size=1)
self.fc = Linear(in_features=1024, out_features=1)
def forward(self, x):
x = self.pool1(F.relu(self.conv1(x)))
x = self.pool2(F.relu(self.conv2_2(F.relu(self.conv2_1(x)))))
x = self.pool3(self.block3_2(self.block3_1(x)))
x = self.block4_3(self.block4_2(self.block4_1(x)))
x = self.pool4(self.block4_5(self.block4_4(x)))
x = self.pool5(self.block5_2(self.block5_1(x)))
x = paddle.reshape(x, [x.shape[0], -1])
x = self.fc(x)
return x
#分析形参定制版本方法
def printStruct(self,x):
print(x.shape)
x = paddle.to_tensor(x) #转为张量x
print(x.shape)
#
x=printItem(self.conv1,x)
x=F.relu(x)
x=printItem(self.pool1,x)
#
x=printItem(self.conv2_1,x)
x=F.relu(x)
x=printItem(self.conv2_2,x)
x=F.relu(x)
x=printItem(self.pool2,x)
#
print('print block3-1:')
x=self.block3_1(x)
print('block3_1.shape=',x.shape)
print('print block3-2:')
x=self.block3_2(x)
print('block3_2.shape=',x.shape)
x=printItem(self.pool3,x)
#
print('print block4_1:')
x=self.block4_1(x)
print('block4_1.shape=',x.shape)
print('print block4_2:')
x=self.block4_2(x)
print('block4_2.shape=',x.shape)
print('print block4_3:')
x=self.block4_3(x)
print('block4_3.shape=',x.shape)
print('print block4_4:')
x=self.block4_4(x)
print('block4_4.shape=',x.shape)
print('print block4_5:')
x=self.block4_5(x)
print('block4_5.shape=',x.shape)
x=printItem(self.pool4,x)
#
print('print block5_1:')
x=self.block5_1(x)
print('block5_1.shape=',x.shape)
print('print block5_2:')
x=self.block5_2(x)
print('block5_2.shape=',x.shape)
x=printItem(self.pool5,x)
x = paddle.reshape(x, [x.shape[0], -1])
x=printItem(self.fc,x)
return x
#=================================
import PM
# 创建模型
model = GoogLeNet()
print(len(model.parameters()))
opt = paddle.optimizer.Momentum(learning_rate=0.001, momentum=0.9, parameters=model.parameters(), weight_decay=0.001)
# 启动训练过程
#PM.train_pm(model, opt)
'''
#输出结果：
PS E:\project\python> & D:/ProgramData/Anaconda3/python.exe e:/project/python/PM/GoogLeNet_PM.py
W0803 18:25:55.522811 8308 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 6.1, Driver API Version: 12.2, Runtime API Version: 10.2
W0803 18:25:55.532805 8308 gpu_resources.cc:91] device: 0, cuDNN Version: 7.6.
116
start training ...
epoch: 0, batch_id: 0, loss is: 0.6920
epoch: 0, batch_id: 20, loss is: 0.8546
[validation] accuracy/loss: 0.7100/0.5381
epoch: 1, batch_id: 0, loss is: 0.6177
epoch: 1, batch_id: 20, loss is: 0.4581
[validation] accuracy/loss: 0.9400/0.3120
epoch: 2, batch_id: 0, loss is: 0.2858
epoch: 2, batch_id: 20, loss is: 0.5234
[validation] accuracy/loss: 0.5975/0.5757
epoch: 3, batch_id: 0, loss is: 0.6338
epoch: 3, batch_id: 20, loss is: 0.3180
[validation] accuracy/loss: 0.9575/0.1915
epoch: 4, batch_id: 0, loss is: 0.1087
epoch: 4, batch_id: 20, loss is: 0.3728
[validation] accuracy/loss: 0.9500/0.2322
PS E:\project\python>
'''
# 输入数据形状是 [N, 3, H, W]
# 这里用np.random创建一个随机数组作为输入数据
x = np.random.randn(*[10,3,224,224])
x = x.astype('float32')
# 创建CNN类的实例，指定模型名称和分类的类别数目
#model = VGG(1)
#
model.printStruct(x);
#
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