PyTorch: import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l
class Inception(nn.Module):
    def __init__(self, in_channels, c1, c2, c3, c4, **kwargs):
        super(Inception, self).__init__(**kwargs)
        self.p1_1 = nn.Conv2d(in_channels, c1, kernel_size=1)
        self.p2_1 = nn.Conv2d(in_channels, c2[0], kernel_size=1)
        self.p2_2 = nn.Conv2d(c2[0], c2[1], kernel_size=3, padding=1)
        self.p3_1 = nn.Conv2d(in_channels, c3[0], kernel_size=1)
        self.p3_2 = nn.Conv2d(c3[0], c3[1], kernel_size=5, padding=2)
        self.p4_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
        self.p4_2 = nn.Conv2d(in_channels, c4, kernel_size=1)
    def forward(self, x):
        p1 = F.relu(self.p1_1(x))
        p2 = F.relu(self.p2_2(F.relu(self.p2_1(x))))
        p3 = F.relu(self.p3_2(F.relu(self.p3_1(x))))
        p4 = F.relu(self.p4_2(self.p4_1(x)))
        return torch.cat((p1, p2, p3, p4), dim=1)
b1 = nn.Sequential(nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),
                    nn.ReLU(),
                    nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b2 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1),
                    nn.ReLU(),
                    nn.Conv2d(64, 192, kernel_size=3, padding=1),
                    nn.ReLU(),
                    nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b3 = nn.Sequential(Inception(192, 64, (96, 128), (16, 32), 32),
                    Inception(256, 128, (128, 192), (32, 96), 64),
                    nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b4 = nn.Sequential(Inception(480, 192, (96, 208), (16, 48), 64),
                    Inception(512, 160, (112, 224), (24, 64), 64),
                    Inception(512, 128, (128, 256), (24, 64), 64),
                    Inception(512, 112, (144, 288), (32, 64), 64),
                    Inception(528, 256, (160, 320), (32, 128), 128),
                    nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b5 = nn.Sequential(Inception(832, 256, (160, 320), (32, 128), 128),
                    Inception(832, 384, (192, 384), (48, 128), 128),
                    nn.AdaptiveAvgPool2d((1,1)),
                    nn.Flatten())
net = nn.Sequential(b1, b2, b3, b4, b5, nn.Linear(1024, 10))
X = torch.rand(size=(1, 1, 96, 96))
for layer in net:
    X = layer(X)
    print(layer.__class__.__name__,'output shape:	', X.shape)


Paddle: import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
class Inception(nn.Layer):
    def __init__(self, in_channels, c1, c2, c3, c4, **kwargs):
        super(Inception, self).__init__(**kwargs)
        self.p1_1 = nn.Conv2D(in_channels, c1, kernel_size=1)
        self.p2_1 = nn.Conv2D(in_channels, c2[0], kernel_size=1)
        self.p2_2 = nn.Conv2D(c2[0], c2[1], kernel_size=3, padding=1)
        self.p3_1 = nn.Conv2D(in_channels, c3[0], kernel_size=1)
        self.p3_2 = nn.Conv2D(c3[0], c3[1], kernel_size=5, padding=2)
        self.p4_1 = nn.MaxPool2D(kernel_size=3, stride=1, padding=1)
        self.p4_2 = nn.Conv2D(in_channels, c4, kernel_size=1)
    def forward(self, x):
        p1 = F.relu(self.p1_1(x))
        p2 = F.relu(self.p2_2(F.relu(self.p2_1(x))))
        p3 = F.relu(self.p3_2(F.relu(self.p3_1(x))))
        p4 = F.relu(self.p4_2(self.p4_1(x)))
        return paddle.concat(x=[p1, p2, p3, p4], axis=1)
b1 = nn.Sequential(nn.Conv2D(1, 64, kernel_size=7, stride=2, padding=3),
                    nn.ReLU(),
                    nn.MaxPool2D(kernel_size=3, stride=2,padding=1))
b2 = nn.Sequential(nn.Conv2D(64, 64, kernel_size=1),
                    nn.ReLU(),
                    nn.Conv2D(64, 192, kernel_size=3, padding=1),
                    nn.ReLU(),
                    nn.MaxPool2D(kernel_size=3, stride=2, padding=1))
b3 = nn.Sequential(Inception(192, 64, (96, 128), (16, 32), 32),
                    Inception(256, 128, (128, 192), (32, 96), 64),
                    nn.MaxPool2D(kernel_size=3, stride=2, padding=1))
b4 = nn.Sequential(Inception(480, 192, (96, 208), (16, 48), 64),
                    Inception(512, 160, (112, 224), (24, 64), 64),
                    Inception(512, 128, (128, 256), (24, 64), 64),
                    Inception(512, 112, (144, 288), (32, 64), 64),
                    Inception(528, 256, (160, 320), (32, 128), 128),
                    nn.MaxPool2D(kernel_size=3, stride=2, padding=1))
b5 = nn.Sequential(Inception(832, 256, (160, 320), (32, 128), 128),
                    Inception(832, 384, (192, 384), (48, 128), 128),
                    nn.AdaptiveAvgPool2D((1, 1)),
                    nn.Flatten())
net = nn.Sequential(b1, b2, b3, b4, b5, nn.Linear(1024, 10))
X = paddle.rand(shape=(1, 1, 96, 96))
for layer in net:
    X = layer(X)
    print(layer.__class__.__name__,'output shape:	', X.shape)