# %%
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.models as models
from torchsummaryX import summary

# %%
alexnet = models.alexnet().cuda()
summary(alexnet, torch.zeros(1, 3, 224, 224).cuda())

# %%
resnet18 = torchvision.models.resnet18().cuda()
summary(resnet18, torch.zeros(4, 3, 32, 32).cuda())

# %%
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output

convnet = Net().cuda()
summary(convnet, torch.zeros(4, 1, 28, 28).cuda())

# %%

# %%
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.models as models
from torchsummaryX import summary

# %%
alexnet = models.alexnet().cuda()
summary(alexnet, torch.zeros(1, 3, 224, 224).cuda())

# %%
resnet18 = torchvision.models.resnet18().cuda()
summary(resnet18, torch.zeros(4, 3, 32, 32).cuda())

# %%
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output

convnet = Net().cuda()
summary(convnet, torch.zeros(4, 1, 28, 28).cuda())

# %%

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